Legal compliance management system and its operating instructions

Abstract

This invention provides a compliance management system that incorporates automated analysis of environmental safety or information safety, as well as instructions for operating it. [Solution] In a management system 2 in which a server device 20 calculates, updates, and stores data and transmits data etc. to peripheral devices 21 and 22 via a network 23 (wired or wireless network), the server device includes a processing module 201, a classification module 203, an analysis module 204, and a communication module 205. The processing module 201 immediately receives first data from the peripheral device in a first period. The classification module 203 classifies the first data according to the classification information. The analysis module 204 analyzes the classified first data and generates an analysis result, and if the analysis result exceeds a threshold, it generates a warning message. The communication module 205 immediately feeds back the warning message to the peripheral device in the first period.

Description

Technical Field

[0001] The present disclosure relates to a compliance management system and an operation method thereof, and more specifically, to a management system that introduces automated analysis of environmental safety or information security and an operation method thereof.

Background Art

[0002] The management of conventional work environments (factories, laboratories, companies, department stores, restaurants, banks, etc.) (environmental safety, information safety, labor safety, etc.) is often carried out manually, lacking systematic and automated management. Therefore, due to human error, the work environment may not comply with the relevant laws and regulations, or in the event of problems in the work environment, it may not be possible to respond immediately, often leading to risks such as fines, work stoppages, and even casualties.

Summary of the Invention

[0003] Some embodiments of the present disclosure relate to a server device including a processing interface, a classification module, an analysis module, and a communication module. The processing interface is configured to immediately receive first data from a peripheral device during a first period. The classification module is configured to classify the first data according to different types. The analysis module is configured to analyze the classified first data to generate an analysis result, and generate a warning message when the analysis result exceeds a threshold value. The communication module is configured to immediately feedback the warning message to the peripheral device during the first period.

[0004] Some embodiments of this disclosure relate to a method for operating a server device, which includes the steps of: receiving first data immediately from a peripheral device in a first period by a processing interface; classifying the first data according to different types by a classification module; analyzing the classified first data and generating analysis results by an analysis module, and generating a warning message if the analysis results exceed a threshold; and immediately feeding back the warning message to the peripheral device in the first period by a communication module. [Brief explanation of the drawing]

[0005] The following describes various embodiments of the model with reference to the drawings, which are not drawn to scale and are illustrative only and do not limit the scope of this disclosure. The reference numerals used in the drawings and specification are illustrative only and do not limit the scope of this disclosure. Identical or similar elements are denoted by the same reference numeral. [Figure 1] A schematic diagram of a management system according to some embodiments of this disclosure is shown. [Figure 2] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Figure 3] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Figure 4] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Figure 5] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Figure 6] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Figure 7] A schematic diagram of a management system according to some embodiments of this disclosure is shown. [Figure 8] A schematic diagram of a management system according to some embodiments of this disclosure is shown. [Figure 9] An operation flowchart of a management system according to some embodiments of this disclosure is shown. [Modes for carrying out the invention]

[0006] The expressions and terms used herein are illustrative only and are not intended to limit the disclosure. Singular or plural forms are illustrative only and are not intended to limit the systems or methods, elements, components, or steps of the disclosure. In this specification, “includes,” “encompasses,” “has,” “contains,” “related to,” and other similar terms encompass the items, equivalents, and additional items listed before them. “Or,” and other similar terms are deemed to refer to any of the items described. In this specification, “one” or “one” is used to describe the units, elements, and components described herein, simply for the sake of clarity and to give a general meaning to the scope of the disclosure. Thus, unless otherwise specified, such descriptions should be understood as including one or at least one, and the singular form also includes the plural form.

[0007] Figure 1 shows a schematic diagram of a management system 1 according to some embodiments of the present disclosure. According to some embodiments of the present disclosure, the management system 1 may be a factory management system, a company management system, a bank management system, a laboratory management system, a restaurant management system, an environmental safety management system, or a management system applied to other locations. The management system 1 includes a server device 10 and one or more peripheral devices 11, 12.

[0008] According to some embodiments of this disclosure, the server device 10 may be a backend device, located at the source of the management system 1, or provided by a third-party cloud platform. In some embodiments, the server device 10 may be configured to compute, update, and store data, and to transmit data etc. to peripheral devices 11, 12 via a network 13 (wired or wireless network). In some embodiments, the peripheral devices 11, 12 may be applied to a factory, laboratory, bank, company, department store, restaurant, or any other location requiring environmental safety management or information safety management. The peripheral devices 11, 12 are configured to collect and record relevant information, and to process or store this information. The peripheral devices 11, 12 may be configured to transmit the collected or processed information to the server device 10 via the network 13 so that the server device 10 can store, manage, and / or process the information. The peripheral devices 11, 12 may obtain necessary data from the server device 10 via the network 13.

[0009] According to some embodiments of this disclosure, the peripheral device 11 may include a processing module 111, a storage module 112, an acquisition module 113, an input module 114, and a communication module 115. In some embodiments, the peripheral device 11 may be, for example, a portable device such as a mobile phone or a personal digital assistant (PDA), or may include such a portable device. In some embodiments, the peripheral device 11 may be, for example, a computer such as a desktop computer, a server, a notebook computer, or a tablet computer, or may include such a computer.

[0010] In some embodiments, the processing module 111, storage module 112, acquisition module 113, input module 114, and communication module 115 may be integrated, and all functions may be realized by a single electronic device. In some embodiments, the processing module 111, storage module 112, acquisition module 113, input module 114, and communication module 115 may be distributed across several devices, and all functions may be realized by multiple devices.

[0011] The processing module 111 may be electrically connected to the storage module 112, the acquisition module 113, the input module 114, and the communication module 115, and may be configured to process (e.g., perform calculations on) the received data. In some embodiments, the processing module 111 may include a processor or processing unit. For example, the processing module 111 may include a central processing unit (CPU), a microcontroller unit (MCU), a graphics processing unit (GPU), etc.

[0012] The memory module 112 may be electrically connected to the processing module 111, the acquisition module 113, the input module 114, and the communication module 115, and configured to store data. In some embodiments, the memory module 111 may include a storage device. For example, the memory module 111 may include a hard disk, a flexible disk, an optical disk, a portable disk, etc. In some embodiments, cloud storage space may be used instead of the memory module 112.

[0013] The acquisition module 113 may be electrically connected to the processing module 111, the storage module 112, the input module 114, and the communication module 115, and configured to acquire environmental information. In some embodiments, the acquisition module 113 may be configured to acquire audio information (e.g., audio frequency, decibel value, etc.), air information (e.g., particle content, concentration of specific substances, etc.), water content information (e.g., concentration of specific substances, etc.), vibration information (e.g., frequency, amplitude, etc.), light information (e.g., frequency, intensity, etc.), soil information (e.g., concentration of specific substances, etc.), electromagnetic wave signals (e.g., frequency, intensity, etc.), electrical signals (e.g., voltage, current, electrical resistance, etc.), and other forms or combinations of the above forms. In some embodiments, the acquisition module 113 may include sensors, microelectromechanical systems (MEMS), etc.

[0014] The input module 114 may be electrically connected to the processing module 111, the storage module 112, the acquisition module 113, and the communication module 115, and configured to allow the user to input data. In some embodiments, the input module 113 may include input devices such as a keyboard, mouse, stylus pen, touchscreen, or microphone to allow the user to input data in the form of text, images, or audio.

[0015] The communication module 115 may be electrically connected to the processing module 111, the storage module 112, the acquisition module 113, and the input module 114, and may be configured to transmit or receive data. In some embodiments, the communication module 115 includes a wired communication device such as an electric wire or optical fiber. In some embodiments, the communication module 115 includes a wireless communication device such as a Wi-Fi module, a mobile network communication module, a Bluetooth module, or a short-range wireless communication module.

[0016] In some embodiments, the peripheral device 11 may further include a power supply module (e.g., a battery), a display module (e.g., a display, a projector), an output module (e.g., a speaker, a printer, a warning light), etc.

[0017] According to some embodiments of the present disclosure, the peripheral device 12 may include a processing module 121, a storage module 122, an input module 123, and a communication module 124. The processing module 121, the storage module 122, the input module 123, and the communication module 124 each have functions similar to those of the processing module 111, the storage module 112, the input module 114, and the communication module 115 included in the peripheral device 11, respectively. Therefore, they will not be described repeatedly here. Although only two peripheral devices 11 and 12 are disclosed in FIG. 1, it should be understood that the management system 1 can include N peripheral devices according to different requirements (N is an integer greater than 1). Note that according to some embodiments of the present disclosure, the types of modules included in the peripheral device can increase or decrease according to requirements and are not limited to those described in FIG. 1.

[0018] According to some embodiments of the present disclosure, the server device 10 may include a processing module 101, a storage module 102, an input module 103, and a communication module 104. The processing module 101, the storage module 102, the input module 103, and the communication module 104 each have functions similar to those of the processing module 111, the storage module 112, the input module 114, and the communication module 115 included in the peripheral device 11, respectively. Therefore, they will not be described repeatedly here. Although only one server device 10 is disclosed in FIG. 1, the management system 1 may include M server devices according to different requirements, and M is an integer greater than 1.

[0019] According to some embodiments of the present disclosure, the peripheral devices 11, 12 can transmit the acquired information or the input data to the server device 10 via the network 13, and the server device 10 can process or store it. The server device 10 can also transmit the processed or stored data to the peripheral devices 11, 12 via the network 13 for the peripheral devices 11, 12 to utilize (e.g., display, manage, store, etc.). According to some embodiments of the present disclosure, the peripheral devices 11, 12 can also store the acquired information or the input data in their storage modules 112, 122 or process it in their processing modules 111, 121.

[0020] FIG. 2 shows an operation flowchart of a management system according to some embodiments of the present disclosure. In some embodiments, the steps or some of the steps disclosed in FIG. 2 can be executed by the management system 1 shown in FIG. 1 or other suitable management systems.

[0021] In step S21, a database is constructed based on the first parameter. In some embodiments, the first parameter may include related normative information such as laws, regulations, administrative rules, and practical guidelines. The above first parameter can be constructed by the server device 10 in FIG. 1. For example, the server device 10 can access related websites (e.g., websites of national regulations databases, judicial courts, labor departments, environmental protection agencies of the Executive Yuan, etc.) to obtain related information (e.g., regulations) on the above websites, and may be configured to store these related information in the storage module 102 by the processing module 101. In some embodiments, the related information may be input by the input module 103 of the server device 10. In some embodiments, these related information is stored in the storage module 102 or a cloud storage device. In some embodiments, these related information can be transmitted to the peripheral devices 11, 12 and stored in the storage modules 112, 122 of the peripheral devices 11, 12.

[0022] In step S22, inspection criteria are generated based on the first parameter. In some embodiments, inspection items that should conform to the norms of relevant laws and regulations and the standards to be achieved can be generated based on the norms of those laws and regulations. For example, based on the provisions of the Water Pollution Control Law, Water Pollution Control Measures and Detection Declaration Management Methods, etc., items to be detected in wastewater discharged from a factory can be generated, and relevant standards (e.g., the concentration of chemical oxygen demand (COD) should be less than 100 ppm) can be generated. For example, based on the provisions of the Air Pollution Control Law, Fixed Pollution Source Installation and Operation Permit Management Methods, etc., items to be detected in exhaust gas discharged from a factory can be generated, and relevant standards (e.g., the concentration of nitrogen oxides (NOx) should be less than 100 ppm) can be generated. For example, based on the provisions of the Labor Standards Law and the Industrial Safety and Health Law, etc., items that employees and employers should comply with (e.g., management of weekly working hours for workers and training hours for specific workers) can be generated. In some embodiments, the inspection criteria can be presented in the form of text, tables, graphics, audio, etc.

[0023] In some embodiments, the above inspection criteria can be generated by the processing module 101 of the server device 10 in Figure 1. For example, the processing module 101 can acquire keywords specified in the regulations (e.g., working hours, wastewater concentration, exhaust gas concentration, etc., as stipulated in the regulations) and automatically generate inspection criteria. In some embodiments, the inspection criteria can be input by the input module 103 of the server device 10. In some embodiments, these inspection criteria are stored in the storage module 102 or a cloud storage device. In some embodiments, these inspection criteria are transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12.

[0024] In step S23, the database is updated based on the second parameter. In some embodiments, the second parameter is an update to the first parameter (e.g., change, deletion, or addition of content). For example, if there are changes (e.g., legal amendments) in relevant normative information such as laws, regulations and administrative rules, and practical circulars, the database constructed in step S21 can be updated by the server device 10 in Figure 1. In some embodiments, the server device 10 may be configured to access relevant websites (e.g., the National Legal Database, the Judicial Yuan website, the Ministry of Labor website, the Environmental Protection Administration website, etc.) and obtain update information from the above websites. In some embodiments, the update information may be input by the input module 103 of the server device 10. In some embodiments, this update information is stored in the storage module 102 or a cloud storage device. In some embodiments, this update information is transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. In some embodiments, the server device 10 may simultaneously send reminder messages to the peripheral devices 11 and 12 to notify them that the information in the original database has been updated.

[0025] In step S24, the inspection criteria are updated based on the second parameter. In some embodiments, the inspection items that should conform to the new standards and the standards to be achieved can be updated based on the content of the legal amendment. In some embodiments, the updated inspection criteria can be generated by the processing module 101 of the server device 10 in Figure 1. For example, the processing module 101 may compare the provisions before and after the legal amendment, obtain the differences, and automatically generate updated inspection criteria based on the differences. In some embodiments, the inspection criteria may be updated by the input module 103 of the server device 10. In some embodiments, these updated inspection criteria are stored in the storage module 102 or a cloud storage device. In some embodiments, these updated inspection criteria are transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. In some embodiments, the server device 10 may simultaneously send a reminder message to the peripheral devices 11 and 12 to notify them that the information in the original database has been updated.

[0026] Currently, factories (or laboratories, companies, department stores, restaurants, etc.) need to consult with experts (e.g., in-house legal staff or external lawyers) to confirm the relevant laws and regulations they must comply with. This is not only time-consuming and labor-intensive, but it also prevents them from immediately grasping any updates to the regulations. As a result, non-compliance with relevant regulations is usually only discovered when an auditing body conducts an audit of the factory, leading to legal liability for the violation, the need to pay administrative fines, and ultimately the risk of operational suspension, business closure, and criminal liability. According to the steps shown in Figure 2 of this disclosure, factories can immediately grasp the laws and inspection standards they must comply with, thereby significantly improving management efficiency and avoiding the risks of legal violations.

[0027] Figure 3 shows an operation flowchart of a management system according to some embodiments of this disclosure. In some embodiments, the steps disclosed in Figure 3 can be performed by management system 1 shown in Figure 1 or other suitable management systems. In some embodiments, the steps shown in Figure 3 can be performed by factory personnel, off-site personnel (e.g., auditors), or jointly.

[0028] In step S31, the items to be detected are provided. In some embodiments, the items to be detected may be generated in step S22 or S24 as shown in Figure 2. In some embodiments, the items to be detected may be generated in other ways. In some embodiments, the items to be detected may be generated by the processing modules 111 and 121 of the peripheral devices 11 and 12. In some embodiments, the items to be detected may be stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. In some embodiments, the items to be detected may be generated by the processing module 101 of the server device 10 and transmitted to the peripheral devices 11 and 12. The items to be detected may be displayed (e.g., by a display module) or output (e.g., by a printer) by the peripheral devices 11 and 12.

[0029] In step S32, detection is performed on the target items. In some embodiments, the user may perform detection on the target items using the acquisition module 113 of the peripheral device 11. For example, the acquisition module 113 may measure the concentration of a specific substance in wastewater or exhaust gas, the decibel value of machine operating noise, etc. In some embodiments, the user may perform detection on the target items using other equipment or other methods. For example, the user may check the length of employees' working hours.

[0030] In step S33, the detection result is recorded. In some embodiments, the peripheral device 11 may automatically store the result detected in step S32 in the storage module 112, or it may transmit the detection result to the server device 10 via the network 13. In some embodiments, the user may input the detection result to the peripheral device 11 using the input module 114.

[0031] In step S34, the detection result is evaluated to determine whether it conforms to the relevant regulations. In some embodiments, the peripheral device 11 automatically compares the detection result with a reference value (for example, the inspection standard generated in step S22 or S24 of Figure 2), and if it conforms to the relevant regulations, it displays that the detection target item is complete (step S35). Otherwise, it may generate a warning for the item that failed detection and generate a new detection target item (step S36). Note that re-detection may be performed for the new detection target items in steps S32 to S34 until all detection items conform to the relevant regulations. In some embodiments, the warning generated in step S36 may be displayed on the management system 1 and / or sent to the relevant person. In some embodiments, if the detection result does not conform to the relevant regulations, the server device 10 or peripheral device 11 is configured to generate a method for improvement for the detection content that does not conform to the relevant regulations, so that the user can perform re-detection according to the improvement method to make the detection result conform to the relevant regulations.

[0032] Currently, employees in factories (or laboratories, companies, department stores, restaurants, etc.) often don't know which items to check if their actions comply with relevant laws. Hiring experts (e.g., external lawyers or technicians) is time-consuming and labor-intensive, and results are not immediately available. Therefore, non-compliance with relevant regulations is usually only discovered when an auditing body conducts an audit of the factory, leading to administrative penalties, and potentially exposing the factory to the risk of operational suspension, business closure, and criminal liability. As shown in Figure 3 of this disclosure, the system automatically generates detection items and improvement methods for any non-compliance, enabling factory employees to regularly and immediately detect and manage these issues. This significantly improves management efficiency and helps avoid the risks associated with legal violations.

[0033] Figure 4 shows an operation flowchart of a management system according to some embodiments of the present disclosure. In some embodiments, the steps disclosed in Figure 4 can be performed by management system 1 shown in Figure 1 or other suitable management systems.

[0034] In step S41, license data is entered. For example, factory licenses, technician licenses, or licenses for personnel outside the factory (suppliers, repair personnel, etc.) are entered into the system. In some embodiments, the server device 10 or peripheral devices 11,12 may access the relevant license issuing website to automatically retrieve the data and store it in the storage modules 102,112,122. In some embodiments, the user may enter the license information using the input modules 103,114,123.

[0035] In step S42, the system detects whether the license information conforms to the relevant regulations. For example, it detects whether the license held by the factory or employee is of a type that allows the current work to be performed. For example, it detects whether the license has expired or is about to expire. If the license information conforms to the provisions of the relevant laws and regulations, the system indicates that the license is valid (step S43). Otherwise, it generates a warning about the non-compliant license (step S44) and notifies the relevant person. In some embodiments, the server device 10 or peripheral device 11 may determine whether the license is valid by automatically comparing the items and years listed in the license with the provisions of the current laws and regulations.

[0036] In step S45, if the license information does not conform to the provisions of the relevant laws and regulations, a warning is generated, and corrective measures are generated based on the relevant regulations. For example, conditions required for license renewal (e.g., payment, training hours, etc.) are generated based on the provisions of the law. After the relevant corrective measures have been implemented, step S42 is repeated until all licenses conform to the relevant regulations.

[0037] Currently, licenses for factories (or laboratories, companies, department stores, restaurants, etc.) are managed almost entirely manually, which carries the risk of oversight and the possibility of licenses becoming invalid due to expiration or changes in regulations. According to the steps shown in Figure 4 of this disclosure, the system can automatically manage licenses and provide methods for correcting invalid licenses, thereby significantly improving management efficiency and avoiding the risk of legal violations.

[0038] Figure 5 shows an operation flowchart of a management system according to some embodiments of the present disclosure. In some embodiments, the steps disclosed in Figure 5 can be performed by management system 1 shown in Figure 1 or other suitable management systems.

[0039] In step S51, a risk event is detected. In some embodiments, the risk event may include fire, power outage, machine failure, gas leak, data anomaly, injury, etc. These risk events may be detected by peripheral devices 11, 12 or other devices.

[0040] When a risk event is detected, the peripheral devices 11 and 12 are configured to generate a warning signal to notify the relevant parties (step S52) and to provide elimination procedures according to the type of risk (step S53). In some embodiments, the warning signal may be transmitted in the form of a light, sound, or message.

[0041] Then, peripheral devices 11 and 12 detect whether the risk has been eliminated (step S54). If it has been eliminated, they turn off the warning signal to notify the relevant parties (step S55). Otherwise, they continue to transmit the warning signal to notify the relevant parties (step S56), and repeat step S54 until it is confirmed that the risk has been eliminated.

[0042] Currently, factories (or laboratories, companies, department stores, banks, restaurants, etc.) have established standard operating procedures for different risks. However, the majority of employees are not necessarily familiar with these procedures, and when risks or accidents occur, they often cannot immediately find a solution, resulting in significant losses or casualties at the factory. According to the steps shown in Figure 5 of this disclosure, when the system detects a risk or accident, it immediately and automatically generates a corresponding standard operating procedure based on the type of risk or accident and notifies the relevant parties. Therefore, any employee can eliminate the risk or accident by following the standard operating procedure, thereby minimizing the damage caused by such risks or accidents.

[0043] Figure 6 shows an operation flowchart of a management system according to some embodiments of the present disclosure. In some embodiments, the steps disclosed in Figure 6 can be performed by management system 1 shown in Figure 1 or other suitable management systems.

[0044] In step S61, external personnel data is constructed. For example, data on personnel outside the factory, such as suppliers and repair technicians, is entered into the system. In some embodiments, the server device 10 or peripheral devices 11, 12 may access the systems of the relevant suppliers or repair companies, automatically acquire the data, and store it in the storage modules 102, 112, 122. In some embodiments, the user may input the data using the input modules 103, 114, 123.

[0045] If an external person wishes to enter the factory, the peripheral devices 11 and 12 are configured to generate a standard entry process based on the external person's attributes (step S62). For example, based on the external person's factory work items, they generate the relevant factory privileges and the steps to be performed. In some embodiments, the generated standard entry process may be displayed (e.g., by a display module) or printed (e.g., by a printer) by the peripheral device.

[0046] In step S63, all actions of the external personnel are recorded to verify whether they are following standard processes. In some embodiments, the actions of the external personnel may be recorded by an image acquisition device (e.g., camera, video camera) or a sensing device, and the processing modules 111 and 121 of the peripheral devices 11 and 12 may determine whether the actions of the external personnel (e.g., the route taken, the steps performed, etc.) conform to the entry standard process. In some embodiments, personnel inside the factory may record the actions of the external personnel, and the processing modules 111 and 121 of the peripheral devices 11 and 12 may determine whether the actions of the external personnel conform to the entry standard process.

[0047] If it is determined that the external personnel are following the standard process, the relevant records are stored (step S62). For example, the recorded data is stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. If it is determined that the external personnel are not following the standard process, a warning signal is generated to notify the relevant parties (step S65), and step S63 is repeated until it is confirmed that the external personnel are following the standard process.

[0048] Currently, factories (or laboratories, companies, department stores, banks, restaurants, etc.) typically manage the entry of external personnel manually, which is not only time-consuming and labor-intensive, but also relatively difficult to record and file. According to the steps shown in Figure 6 of this disclosure, the system can automatically generate standard entry processes for external personnel with different purposes, and automatically track and record them, thereby significantly improving management efficiency and reducing labor costs.

[0049] In some embodiments, the methods or steps described in Figures 2-6 may be executed by a shared resource sharing program having multiple program instructions (code). The shared resource sharing program is a computer program product that can be stored on a non-transitory computer-readable storage medium. When these program instructions are loaded into an electronic device (such as the server device 10 and peripheral devices 11, 12 shown in Figure 1), the computer program executes the methods or steps shown in Figures 2-6.

[0050] In some embodiments, if a factory (or any location requiring environmental safety management, such as a laboratory, company, department store, bank, or restaurant) has multiple locations (or branches, offices, or bank branches), each location may have one or more peripheral devices 11,12, which perform the steps shown in Figures 2-6 to integrate data from each location and transmit the integrated data to all locations, a designated location (e.g., the company's headquarters), and / or a server device 10. The integrated data allows for the verification of individual information for all locations (e.g., items that do not comply with regulations at each location) and aggregate information for all locations (e.g., the number of invalid licenses at all locations).

[0051] In some embodiments, if a factory (or any location requiring environmental safety management, such as a laboratory, company, department store, bank, or restaurant) has multiple locations (or branches, offices, or bank branches), one of these locations may use peripheral devices 11 and 12 as an internal server, connecting to the server device 10 via the network 13 to transmit data. Other locations may use electronic devices similar to peripheral devices 11 and 12 (e.g., computers, tablet PCs, mobile phones, etc.) to connect to the internal server via the internal network to transmit data. The internal server integrates the information from the other locations and transmits it to all locations, a designated location (e.g., the company's headquarters), and / or the server device 10. The integrated data allows for verification of individual information from all locations (e.g., items that do not comply with regulations at each location) and aggregated information from all locations (e.g., the number of invalid licenses at all locations). Since the information from the other locations is transmitted to the internal server via the internal network rather than directly to the server device 10 via the network 13, a higher level of security is ensured.

[0052] Figure 7 shows a schematic diagram of a control system 2 according to some embodiments of the present disclosure. Similar to control system 1 shown in Figure 1, control system 2 may be a factory control system, company control system, bank control system, laboratory control system, restaurant control system, environmental safety control system, medical control system, vehicle operation control system, or a control system applied to other locations. Control system 2 includes a server device 20 and one or more peripheral devices 21, 22. Steps or operations performed by control system 1 shown in Figure 1 may be similarly performed by control system 2.

[0053] According to some embodiments of this disclosure, the server device 20 is a backend device and may be located at the source of the management system 2 or provided by a third-party cloud platform. In some embodiments, the server device 20 may be configured to compute, update, and store data and transmit data etc. to peripheral devices 21, 22 via a network 23 (wired or wireless network).

[0054] Peripheral devices 21 and 22 are Internet of Things (IoT) devices. Peripheral devices 21 and 22 may be applied to exemplary locations of the management system 2, or to any other location requiring environmental safety management or information safety management. Peripheral devices 21 and 22 can be operated by a graphical user interface to acquire user-side data (e.g., raw data). The graphical user interface may be displayed, for example, on the control panel, dashboard, weighing plate, or other similar display of the peripheral devices 21 and 22.

[0055] Peripheral devices 21 and 22 are configured to sense environmental data of the surrounding location, record relevant information, and process or store this information. Peripheral devices 21 and 22 are configured to immediately transmit the sensed or processed information to server device 20 via network 23 so that server device 20 can store, classify, and / or analyze it. Server device 20 immediately feeds back the analyzed data to peripheral devices 21 and 22 via network 23.

[0056] According to some embodiments of this disclosure, the peripheral device 21 includes a sensing module 211, a storage module 212, a processing module 213, and a communication module 214. The sensing module 211, storage module 212, processing module 213, and communication module 214 are electrically coupled to each other. The peripheral device 21 collects or senses user-side data from the surrounding environment, user input, or user usage patterns, and exchanges the data with the server device 20 via its application program and network 23. In some embodiments, the peripheral device 21 is a factory management device. The peripheral device 21 may be a vehicle. The peripheral device 21 includes, for example, a surveillance camera, a surveillance television, a portable device, or sports equipment. The peripheral device 21 may include, for example, a mobile phone or a PDA. The peripheral device 21 is, for example, a computer such as a desktop computer, a server, a notebook computer, or a tablet computer, or may include such a computer.

[0057] In some embodiments, the sensing module 211, storage module 212, processing module 213, and communication module 214 are integrated, and all functions are realized by a single electronic device. In some embodiments, the sensing module 211, storage module 212, processing module 213, and communication module 214 are distributed across several devices, and all functions are realized by multiple devices.

[0058] The sensing module 211 is configured to sense and acquire environmental information about the peripheral device 21 itself and its surroundings. The environmental information is user-side raw data, or may include user-side raw data. The raw data includes text, numbers, images, video, or audio.

[0059] In some embodiments, environmental information includes resource usage information (e.g., electricity consumption, water consumption, and oil consumption of the peripheral device 21 itself, or electricity consumption, water consumption, and oil consumption of other devices near the peripheral device 21), temperature information (e.g., operating temperature of the peripheral device 21 itself, or operating temperature of other devices near the peripheral device 21), liquid element content information (e.g., content and concentration of specific elements in the working tank of the peripheral device 21, or content and concentration of specific elements in liquids in the environment near the peripheral device 21), personnel recognition information (e.g., user face recognition, voice recognition), voice information (frequency, decibel value of the operating sound of the peripheral device 21 itself, or This includes the frequency and decibel value of the operating sounds of other devices near the peripheral device 21, air information (e.g., particle content in the surrounding air, concentration of specific substances, etc.), vibration information (e.g., operating frequency and amplitude of the peripheral device 21 itself, or operating frequency and amplitude of other devices near the peripheral device 21), light information (e.g., frequency and intensity, etc.), soil information (e.g., concentration of specific substances, etc.), electromagnetic wave signals (e.g., operating frequency and intensity of the peripheral device 21 itself, or operating frequency and intensity of other devices near the peripheral device 21, etc.), electrical signals (e.g., voltage, current, electrical resistance, etc.), other forms, or combinations of the above forms. In some embodiments, the sensing module 211 includes sensors, MEMS, smart clamp meters, etc.

[0060] In some embodiments, the sensing module 211 is configured to acquire data manually entered by the user. The sensing module 211 may also be configured to acquire the necessary data via the network 23.

[0061] The storage module 212 may be configured to store data acquired by the sensing module 211. In some embodiments, the storage module 212 may include a storage device or memory device. For example, the storage module 212 may include a hard disk, a flexible disk, an optical disk, a portable disk, etc. In some embodiments, cloud storage space may be used instead of the storage module 212.

[0062] The processing module 213 may be configured to process (e.g., perform calculations on) the data acquired by the sensing module 211. In some embodiments, the processing module 213 may include a processor or processing unit. For example, the processing module 213 may include a central processing unit (CPU), a microcontroller (MCU), a graphics processor (GPU), etc.

[0063] The communication module 214 may be configured to transmit or receive data. In some embodiments, the communication module 214 includes a wired communication device such as an electric wire or optical fiber. In some embodiments, the communication module 214 includes a wireless communication device such as a Wi-Fi module, a mobile network communication module, a Bluetooth module, or a short-range wireless communication module.

[0064] In some embodiments, the peripheral device 21 further includes a power supply module (e.g., a battery), a display module (e.g., a display, a projector), an input module (e.g., a keyboard, mouse, stylus pen, touchscreen, microphone, etc., for the user to input data in the form of text, images, video, or audio), an output module (e.g., a speaker, printer, warning light), etc.

[0065] According to some embodiments of this disclosure, the peripheral device 22 includes a sensing module 221, a storage module 222, a processing module 223, and a communication module 224. Since the peripheral device 22 has functions similar to those of the peripheral device 21, it will not be described again here.

[0066] According to some embodiments of this disclosure, the server device 20 includes a processing module 201, a storage module 202, a classification module 203, an analysis module 204, and a communication module 205, the processing module 201, the storage module 202, the classification module 203, the analysis module 204, and the communication module 205 being electrically coupled to one another. Although only one server device 20 is disclosed in Figure 7, the management system 2 may include M server devices depending on different needs, where M is an integer greater than 1.

[0067] Processing module 201 includes an input interface, an output interface, a processor, and a processing unit. For example, processing module 223 includes a central processing unit (CPU), a microcontroller (MCU), a graphics processor (GPU), etc.

[0068] The storage module 202 includes a storage device or memory device. For example, the storage module 222 includes a hard disk, a flexible disk, an optical disk, a portable disk, etc. In some embodiments, cloud storage space may be used instead of the storage module 222.

[0069] The classification module 203 includes memory, a processor, a processing unit, and a classifier model, the classifier model may be configured to perform coarse and fine classification. The analysis module 204 includes memory, a processor, a processing unit, and an artificial intelligence (AI) analysis model, the AI ​​analysis model including a neural network-based image processing engine, a speech processing engine, a feature acquisition engine, and a natural language processing (NLP) engine. The image processing engine of the AI ​​analysis model in the analysis module 204 may include an image processing engine and a video processing engine. The communication module 205 includes a wired communication device or a wireless communication device.

[0070] According to some embodiments of this disclosure, peripheral devices 21 and 22 immediately transmit acquired information or input data to server device 20 via network 23, and store and analyze it via server device 20. Server device 20 may immediately feed back the processed and analyzed data to peripheral devices 21 and 22 via network 23 for use by the peripheral devices 21 and 22 (e.g., display, manage, store, etc.).

[0071] For example, the processing module 201 of the server device 20 is configured to immediately receive first data from the peripheral device 21 during the first period and store the first data in the storage module 202. The storage module 202 may be configured to store the first data. The storage module 202 is configured to store classification information used for detection. The storage module 202 is configured to store classification information associated with the environmental information of the peripheral device 21. In some embodiments, the classification information may be a coarse classification and may include classification types such as text, numbers, images, videos, and audio. The classification information may also be a fine classification and may include combinations of aspects corresponding to the environmental information of the peripheral device 21, so this will not be repeated here. The fine classification may include detection criteria (e.g., specified conformance and nonconformance criteria), error messages, and limiting conditions corresponding to the environmental information of the peripheral device 21. The limiting conditions may include time conditions, location conditions, event conditions, number of people conditions, power conditions, item component conditions, air component conditions, or liquid component conditions, and combinations thereof. The memory module 202 may be configured to store thresholds associated with the environmental information of the peripheral device 21.

[0072] The classification module 203 of the server device 20 is configured to acquire first data from the storage module 202 and classify the first data according to different types in the classification information. The classification module 203 is configured to roughly classify the first data according to the rough classification settings of the classification information. The classification module 203 is configured to roughly classify the first data according to the rough classification combinations of the classification information. The classification module 203 is configured to refine the first data according to the detailed classification settings of the classification information. The classification module 203 is configured to refine the first data according to the detailed classification combinations of the classification information. The analysis module 204 is configured to analyze the classified first data and generate analysis results based on thresholds stored in the storage module 202. The analysis module 204 is configured to generate a warning message if the analysis results exceed the thresholds.

[0073] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a smart clamp meter. The smart clamp meter can be used to collect resource usage information of the peripheral device 21 during a first period (e.g., electricity usage, water usage, and oil usage of the peripheral device 21 itself, or electricity usage, water usage, and oil usage of other devices near the peripheral device 21). The processing module 213 is configured to process the resource usage information acquired by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 may be configured to immediately transmit the first data to the processing module 201 of the server device 20 via the network 23 during the first period.

[0074] The processing module 201 of the server device 20 is configured to immediately receive the first data during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into text and numerical data according to different types in the classification information.

[0075] The analysis module 204 is configured to analyze classified first data based on a first threshold stored in the memory module 202 and generate analysis results. If the first data exceeds the first threshold, the analysis results indicate that the resource usage information of the peripheral device 21 has exceeded the upper limit, and the analysis module 204 is configured to generate a warning message. The communication module 205 is configured to immediately feed back the warning message to the peripheral device 21 during the first period. This allows the peripheral device 21 to immediately receive the warning message and inform the user that the resource usage information (e.g., electricity usage, water usage, or oil usage) has exceeded the upper limit. In this case, the user may perform detection on the peripheral device 21 to determine whether the sudden increase in electricity usage, water usage, or oil usage is due to a failure or damage to a component of the peripheral device 21.

[0076] The sensing module 211 of the peripheral device 21 is used to collect resource usage information of the peripheral device 21 during the second period, and the processing module 213 is configured to process the resource usage information acquired by the sensing module 211 into second data, with the second period being the period following the first period. Similarly, the processing module 201 of the server device 20 is configured to immediately receive the second data during the second period and store the second data in the storage module 202. The classification module 203 is configured to classify the second data into text and numerical data according to different types in the classification information. The analysis module 204 is configured to analyze the classified second data and generate analysis results based on the second threshold and first data stored in the storage module 202. If the difference between the sum of the second data and the sum of the first data exceeds the second threshold, the analysis result indicates that the resource usage information of the peripheral device 21 during the second period has exceeded a certain upper limit of the resource usage information during the first period, and the analysis module 204 is configured to generate a warning message. The communication module 205 is configured to immediately feed back a warning message to the peripheral device 21 during the second period. This allows the peripheral device 21 to immediately receive the warning message and notify the user that the resource usage information (e.g., electricity usage, water usage, or oil usage) during the second period has exceeded a specific upper limit for resource usage information during the first period. In this case, the user may perform detection on the use of the peripheral device 21 during the second period to confirm whether the use of the peripheral device 21 during the second period was expected.

[0077] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a temperature control dashboard and a video camera. The video camera can detect the temperature control dashboard of the peripheral device 21 itself or of other nearby devices. The processing module 213 may be configured to process the temperature information acquired by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 may be configured to immediately transmit the first data to the processing module 201 of the server device 20 via the network 23 during a first period.

[0078] The processing module 201 of the server device 20 is configured to immediately receive the first data during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into images and videos according to different types in the classification information using the image processing engine and video processing engine of the AI ​​analysis model.

[0079] The analysis module 204 is configured to apply multiple images or videos from the first data to the image processing engine of the AI ​​analysis model and to determine multiple regions of interest in each image. The multiple regions of interest may be specific temperature meters on a temperature control dashboard. The image processing engine of the AI ​​analysis model of the analysis module 204 determines the D of the nth region of interest in the mth image. mn It is configured to determine descriptors, and each descriptor is associated with a display value in each image. The display values ​​may be associated with the chromaticity and luminance of pixels (including pixels of red, green, blue, white, etc.). The image processing engine of the AI ​​analysis model of the analysis module 204 processes these D mn The system is configured to determine whether the operation of the peripheral device 21 is normal or abnormal based on a comparison between the descriptor and the threshold stored in the memory module 202. mn If the descriptor exceeds a threshold, the analysis results indicate that the operating temperature of the peripheral device 21 has exceeded the upper limit, and the analysis module 204 is configured to generate a warning message. The communication module 205 is configured to immediately feed back the warning message to the peripheral device 21 during the first period. This allows the peripheral device 21 to immediately receive the warning message and inform the user that the temperature information (e.g., the operating temperature of the peripheral device 21 itself, or the operating temperature of other devices near the peripheral device 21) has exceeded the upper limit. In this case, the user may perform detection on the peripheral device 21 to determine whether the temperature rise is due to a failure or damage to a component of the peripheral device 21 or a program problem.

[0080] The sensing module 211 of the peripheral device 21 is used to collect temperature information of the peripheral device 21 during the second period, and the processing module 213 is configured to process the temperature information acquired by the sensing module 211 into second data, with the second period being the period following the first period. Similarly, the processing module 201 of the server device 20 is configured to immediately receive the second data during the second period and store the second data in the storage module 202. Similarly, the image processing engine of the AI ​​analysis model of the analysis module 204 is configured to analyze the second data to determine whether the operation of the peripheral device 21 is normal or abnormal.

[0081] According to some embodiments of the present disclosure, the sensing module 211 of the peripheral device 21 includes a sensor provided in the work tank of the peripheral device 21, which is used to detect information on the content of liquid elements in the work tank (for example, the content or concentration of a specific element in the work tank of the peripheral device 21, or the content or concentration of a specific element in the liquid in the environment near the peripheral device 21), where the specific element is, for example, chlorine, sulfur, or any other element that the peripheral device 21 intends to detect. The processing module 213 is configured to process the liquid element content information acquired by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 is configured to immediately transmit the first data to the processing module 201 of the server device 20 via the network 23 during a first period.

[0082] The processing module 201 of the server device 20 is configured to immediately receive the first data during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into text and numerical data according to different types in the classification information. The classification module 203 is configured to classify the first data into multiple first datasets according to the constraints stored in the storage module 202.

[0083] The analysis module 204 is configured to determine whether the content of a first element (e.g., chlorine) in the peripheral device 21 is normal or not, based on a comparison between a first detection criterion group stored in the memory module 202 and a first data set from among a plurality of first data sets, and to generate a first analysis result. The analysis module 204 is configured to determine whether the content of a second element (e.g., sulfur) in the peripheral device 21 is normal or not, based on a comparison between a second detection criterion group stored in the memory module 202 and a second data set from among a plurality of first data sets, and to generate a second analysis result.

[0084] If the first analysis result exceeds the first detection criterion group, the analysis result indicates that the content of the first element in the peripheral device 21 has exceeded the upper limit, and the analysis module 204 is configured to generate a first warning message. Similarly, if the second analysis result exceeds the second detection criterion group, the analysis result indicates that the content of the second element in the peripheral device 21 has exceeded the upper limit, and the analysis module 204 is configured to generate a second warning message. The communication module 205 is configured to immediately feed back the first and second warning messages to the peripheral device 21 during the first period. This allows the peripheral device 21 to immediately receive the first and second warning messages and inform the user that the liquid element content information (e.g., chlorine, sulfur) in the work tank has exceeded the upper limit. In this case, the user may perform detection on the peripheral device 21 to determine whether the increase in chlorine and sulfur content is due to a malfunction or damage to a component of the peripheral device 21.

[0085] Similarly, the peripheral device 21 and the server device 20 can repeat their operations in the second period following the first period.

[0086] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a video camera and a decibel meter. The video camera and decibel meter can detect images and sounds of the peripheral device 21 itself or users or other workers of other nearby devices. The processing module 213 may be configured to process the personnel recognition information or sound information acquired by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 may be configured to immediately transmit the first data to the processing module 201 of the server device 20 via the network 23 during a first period.

[0087] The processing module 201 of the server device 20 is configured to immediately receive the first data during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into images, videos, and audio according to different types in the classification information.

[0088] The analysis module 204 is configured to apply multiple images or videos from the first data to the image processing engine of the AI ​​analysis model to determine multiple regions of interest (e.g., faces or identification) in each image. The image processing engine of the AI ​​analysis model of the analysis module 204 determines the D of the nth region of interest in the mth image. mn It is configured to determine descriptors, and each descriptor is associated with a display value in each image. The image processing engine of the AI ​​analysis model of the analysis module 204 uses these D mn Based on a comparison between the descriptor and one group of detection criteria stored in the memory module 202, it is configured to determine whether the identity of the user of the peripheral device 21 matches. The image processing engine of the AI ​​analysis model of the analysis module 204 uses these D mnThe peripheral device 21 is configured to determine whether the equipment worn by the user conforms to the detection criteria based on a comparison between a descriptor and a group of detection criteria stored in the memory module 202. If the analysis module 204 determines that the user's identity does not match or the equipment does not conform to the detection criteria, the analysis module 204 is configured to generate a warning message. The communication module 205 is configured to immediately feed back the warning message to the peripheral device 21 during the first period. This allows the peripheral device 21 to immediately receive the warning message and warn the user.

[0089] The analysis module 204 is configured to apply multiple audio files in the first data to the speech processing engine of the AI ​​analysis model to determine multiple voice segments of interest (e.g., the user's voice) within each audio file. Based on a group of detection criteria stored in the memory module 202, it determines whether the multiple voice segments of interest match the group of detection criteria. If the analysis module 204 determines that the multiple voice segments of interest do not match the group of detection criteria, it is configured to generate a warning message. The communication module 205 is configured to immediately feed back the warning message to the peripheral device 21 during the first period. This allows the peripheral device 21 to immediately receive the warning message and warn the user.

[0090] The analysis module 204 is configured to determine whether multiple audio segments of interest have exceeded a threshold based on thresholds stored in the memory module 202. If the analysis module 204 determines that multiple audio segments of interest have exceeded a threshold, it is configured to generate a warning message. The communication module 205 is configured to immediately feed back the warning message to the peripheral device 21 during the first period. In this case, the user may perform detection on the peripheral device 21 to determine whether the increase in the frequency or decibel value of the operating sound is due to a failure or damage to a component in the peripheral device 21.

[0091] Similarly, the peripheral device 21 and the server device 20 may repeat their operations in the second period following the first period.

[0092] Figure 8 shows a schematic diagram of a management system 2' according to some embodiments of the present disclosure. The management system 2' in Figure 8 is the same as the management system 2 in Figure 7, except that the server device 20' of the management system 2' additionally includes a generator module 206 and a link generation module 207. The generator module 206 and the link generation module 207 are electrically coupled to each other with the processing module 201, the storage module 202, the classification module 203, the analysis module 204, and the communication module 205.

[0093] The generator module 206 is configured to generate identifiers based on classified first data during a first period, and to generate integrated data based on these identifiers. The identifiers and first data are associated. The integrated data may include the first data and analysis results. The link generation module 207 is configured to convert the integrated data into a URL link and link this URL link to the peripheral device 21. The integrated data and the URL link are stored in the storage module 202 as traceability data, making it easier for the user to trace and understand the historical information and analysis results.

[0094] In another embodiment, the traceability function may be used to provide an early warning to the peripheral device 21 about whether or not the sensing module 211 needs to be replaced. Under normal use, the degradation of the sensitivity of the sensing module 211 affects the sensing data. By comparing the usage data of the sensing module 211 with the traceability data, it is possible to determine whether or not the sensing module 211 needs to be replaced. The usage data of the sensing module 211 includes information such as the purchase date, the new product lifespan of the module 211, and the start date of use of the sensing module 211. After purchase, the usage data of the sensing module 211 is stored in the storage module 202. After replacement (i.e., replacing the old sensing module 211 with a new sensing module 211), the replacement date of the sensing module 211 is stored in the storage module 202. The actual years of use of the sensing module 211 can be determined from the start date of use of the sensing module 211 and the replacement date of the sensing module 211 (i.e., by subtracting the start date of use of the sensing module 211 from the replacement date of the sensing module 211).

[0095] After the sensing module 211 is put into use, sensing data is generated, and during use (which may include several periods), traceability data is stored in the storage module 202 by the generator module 206 and the link generation module 207, thereby determining the actual years of use of the sensor. If the actual years of use are one year, 11 months after the sensing module 211 is put into use, the server device 20' checks whether the sensing data values ​​from the 9th to 11th month of the sensing module 211 were abnormally high, thereby confirming whether the sensing module 211 is highly faulty. This confirmation makes it possible to predict whether the sensing module 211 has deteriorated, and by immediately replacing the sensor module 211 when the deterioration has progressed excessively and is likely to affect sensitivity and accuracy, it is possible to prevent the impact on the accuracy of the sensing data and reduce costs.

[0096] In some embodiments, if a factory (or any location requiring environmental safety management, such as a laboratory, company, department store, bank, or restaurant) has multiple locations (or branches, offices, bank branches, etc.), one of these locations may use peripheral devices 21, 22 as an internal server, connecting to a server device 20' via network 23 to transmit data, while other locations may use electronic devices similar to peripheral devices 21, 22 (e.g., computers, tablet PCs, mobile phones, etc.) to connect to the internal server via the internal network to transmit data. Information from the other locations is integrated via the internal server and transmitted to all locations, a designated location (e.g., the company's headquarters), and / or server device 20'. In some embodiments, individual information for all locations can be viewed using only the integrated data generated by the generator module 206, without going through URL links converted by the link generation module 207, and aggregated information for all locations can also be viewed. Since information from the other locations is transmitted to the internal server via the internal network, rather than directly to the server device 20' via network 23, a higher level of security is ensured.

[0097] Figure 9 shows an operation flowchart of a management system according to some embodiments of the present disclosure. In some embodiments, the methods or some operations disclosed in Figure 9 may be performed by management system 2 shown in Figure 7 or management system 2' shown in Figure 8.

[0098] In operation S71, during the first period, the processing module 201 of the server device 20 of the management system 2 immediately receives first data from peripheral devices 21 and 22. The first data may be stored in the storage module 202.

[0099] In operation S72, the classification module 203 classifies the first data according to one or a combination thereof of the classification information, detection criteria, error messages, and limiting conditions stored in the storage module 202.

[0100] In operation S73, the analysis module 204 analyzes the classified first data to generate analysis results, and if the analysis results exceed a threshold, it generates a warning message.

[0101] In operation S74, the communication module 205 immediately feeds back a warning message to the peripheral devices 21 and 22 during the first period.

[0102] The server device 20 of the management system 2 can repeat operations S71 to S74 on peripheral devices 21 and 22 during the second period.

[0103] While the technical content and features of this disclosure have been described above, a person with ordinary skill in the art of this disclosure may make various changes and modifications without departing from the teachings and disclosures of this disclosure. Accordingly, the scope of this disclosure is not limited to the disclosed embodiments, but includes other changes and modifications that do not depart from this disclosure, and is defined by the following claims. [Explanation of symbols]

[0104] 1: Management System 2: Management System 2': Management System 10: Server equipment 11: Peripheral devices 12: Peripheral devices 13: Network 20: Server equipment 20': Server device 21: Peripheral devices 22: Peripheral devices 23: Network 101: Processing Module 102: Memory Module 103: Input Module 104: Communication module 111: Processing Module 112: Memory Module 113: Acquisition Module 114: Input Module 115: Communication module 121: Processing Module 122: Memory Module 123: Input Module 124: Communication module 201: Processing Module 202: Memory Module 203: Classification Module 204: Analysis Module 205: Communication module 206: Generator Module 207: Link Generation Module 211: Sensing module 212: Memory Module 213: Processing Module 214: Communication module 221: Sensing module 222: Memory Module 223: Processing Module 224: Communication module S21: Step S22: Step S23: Step S24: Step S31: Step S32: Step S33: Step S34: Step S35: Step S36: Step S41: Step S42: Step S43: Step S44: Step S45: Step S51: Step S52: Step S53: Step S54: Step S55: Step S56: Step S61: Step S62: Step S63: Step S64: Step S65: Step S66: Step S71: Step S72: Step S73: Step S74: Step

Claims

1. A processing module configured to immediately receive first data from a peripheral device during the first period, A classification module configured to classify the first data according to classification information, An analysis module is configured to analyze the first data classified based on a threshold to generate an analysis result, and to generate a warning message if the analysis result exceeds the threshold. A communication module configured to immediately feed back the warning message to the peripheral device during the first period, A server device that includes this.

2. The system further includes a storage module configured to store the first data, The memory module is further configured to store the classification information, the threshold, the first detection criterion group, the second detection criterion group, and a plurality of limiting conditions. The classification module is configured to classify the first data into a plurality of first datasets according to the plurality of constraints. The server device according to claim 1.

3. The server device according to claim 2, wherein the classification module is configured to classify a plurality of first datasets into a plurality of first data subsets according to the first detection criterion group.

4. The first data includes a plurality of images, the plurality of images in the first data are associated with the peripheral device, and the analysis module is The aforementioned multiple images are applied to an image processing model to determine multiple regions of interest in each image, Each is associated with a display value in each image, and the nth region of interest of the mth image is D ｍｎ Determine the descriptor, According to the image processing model, D ｍｎ The server device according to claim 1, configured to determine whether the operation of the peripheral device is normal or abnormal based on a comparison between a descriptor and the threshold.

5. The server device according to claim 1, further comprising a generator module configured to generate identifiers associated with the classified first data based on the first data, and to generate integrated data including the first data and the analysis results based on the identifier.

6. The server device according to claim 5, further comprising a link generation module configured to convert the integrated data into a URL link and to link the URL link to the peripheral device.

7. The server device according to claim 2, wherein the plurality of limiting conditions include time conditions, location conditions, event conditions, number of people conditions, power conditions, material composition conditions, air composition conditions, or liquid composition conditions.

8. The plurality of constraints include time conditions and liquid component conditions, and the classification module is configured to classify the first data into the plurality of first datasets according to the time conditions and the liquid component conditions. The analysis module is configured to determine whether the content of a first element in the peripheral device is normal based on a comparison between the first detection criterion group and the first data set among the plurality of first data sets, and to determine whether the content of a second element in the peripheral device is normal based on a comparison between the second detection criterion group and the second data set among the plurality of first data sets. The server device according to claim 2.

9. The processing module is configured to receive second data from the peripheral device in a second period following the first period. The classification module is configured to classify the second data according to the classification information and to classify the second data into a plurality of second datasets according to the power conditions in the plurality of limiting conditions. The analysis module is configured to generate the analysis results by comparing the plurality of first datasets with the plurality of second datasets, and to generate the warning message if the difference between the sum of the plurality of second datasets and the sum of the plurality of first datasets exceeds the threshold. The communication module is configured to immediately feed back the warning message to the peripheral device during the second period. The server device according to claim 2.

10. The server device according to claim 1, wherein the first data is acquired by the sensing module of the peripheral device.

11. The processing module performs the steps of immediately receiving first data from a peripheral device during the first period, The classification module performs the step of classifying the first data according to the classification information, The analysis module analyzes the classified first data to generate analysis results, and if the analysis results exceed a threshold, it generates a warning message. The communication module immediately feeds back the warning message to the peripheral device during the first period, A method for operating a server device, including [specific details omitted].

12. The first data received by the processing module is stored in the storage module. The memory module is configured to store the classification information, the threshold, the first detection criterion group, the second detection criterion group, and a plurality of limiting conditions. The method according to claim 11, wherein the classification module is configured to classify the first data into a plurality of first datasets according to the plurality of constraints.

13. The method according to claim 12, wherein the classification module is configured to classify a plurality of first datasets into a plurality of first data subsets according to the first detection criterion group.

14. The first data includes a plurality of images, the plurality of images in the first data are associated with the peripheral device, and the method is, The operation involves applying the aforementioned multiple images to an image processing model to determine multiple regions of interest in each image, Each is associated with a display value in each image, and D is the j-th region of interest in the i-th image. ｉｊ The operation of determining the descriptor, According to the calculation model, D ｉｊ The method according to claim 11, further comprising performing by the analysis module an operation to determine whether the operation of the peripheral device is normal or abnormal based on a comparison of a descriptor with the threshold.

15. The generator module generates identifiers associated with the classified first data based on the first data, and generates integrated data including the first data and the analysis results based on the identifiers. The communication module transmits the integrated data to the peripheral device, The method according to claim 11, further comprising:

16. The link generation module performs the steps of converting the integrated data into a URL link, The steps include associating the URL link with the first data stored in the storage module, The steps include linking the aforementioned URL link to the peripheral device, The method according to claim 15, further comprising:

17. The method according to claim 12, wherein the plurality of limiting conditions include time conditions, location conditions, event conditions, number of people conditions, power conditions, article composition conditions, air composition conditions, or liquid composition conditions.

18. The aforementioned limiting conditions include time conditions and liquid component conditions, and the method is An operation to classify the first data into the plurality of first datasets according to the time conditions and the liquid component conditions, An operation to determine whether the content of the first element in the peripheral device is normal based on a comparison between the first detection criterion group and the first data set among the plurality of first data sets, The method according to claim 12, further comprising the classification module performing the operation of determining whether the content of the second element in the peripheral device is normal based on a comparison of the second detection criterion group with the second data set among the plurality of first data sets.

19. The processing module performs the steps of receiving second data from the peripheral device in a second period following the first period, The classification module classifies the second data according to the classification information and classifies the second data into a plurality of second datasets according to the power conditions in the plurality of limiting conditions, The analysis module generates the analysis results by comparing the plurality of first datasets with the plurality of second datasets, and if the difference between the sum of the plurality of second datasets and the sum of the plurality of first datasets exceeds the threshold, it generates the warning message. The communication module provides immediate feedback of the warning message to the peripheral device during the second period. The method according to claim 12, further comprising:

20. The method according to claim 11, wherein the first data is acquired by the sensing module of the peripheral device.

Images