Temperature monitoring system and method thereof

Abstract

The application provides a temperature monitoring system and a method thereof. The temperature monitoring system comprises a sensing layer and an edge server. A switchable resistance network is arranged in a collection terminal, and a switch component is used to selectively connect the sensor and the resistance network. A master control unit can automatically identify the temperature sensor model and the wiring mode based on the on-off state of the switch, thereby realizing adaptive collection of different models of sensors and reducing measurement errors caused by mismatched sensor types. Meanwhile, the edge server performs local time sequence trend analysis on the temperature detection data and generates early warning information when an anomaly is identified, thereby maintaining the continuity of monitoring and warning in the event of external network interruption, realizing on-demand cloud uploading through a determined cloud uploading strategy, reducing bandwidth occupation, lowering operating costs, improving the security of sensitive data in the local area network, reducing dependence on the cloud, and improving reliability and operability in complex fields.

Description

Technical Field

[0001] This application relates to the field of detection technology, and in particular to a temperature monitoring system and method thereof. Background Technology

[0002] Temperature, as a crucial parameter for the safe operation of power equipment, cold chain quality control, stability of precision manufacturing processes, and reliability of laboratory environments, is widely collected and monitored in scenarios such as power cabinets, cold chain storage, precision manufacturing workshops, and laboratory environmental monitoring. The accuracy, real-time performance, and security of temperature data directly impact key business objectives such as equipment fault early warning, product quality traceability, and compliance auditing. Therefore, the industry is gradually adopting wireless temperature monitoring solutions to reduce wiring complexity, improve deployment efficiency, and achieve centralized management of distributed monitoring points.

[0003] Traditional wireless temperature monitoring systems typically employ a direct connection between the terminal and the cloud. This reliance on public network connectivity makes it difficult to achieve independent local monitoring, trend analysis, and early warning within a local area network. External network interruptions can easily impact data continuity and alarm timeliness. Furthermore, continuously uploading full temperature data to the cloud not only consumes bandwidth and increases operating costs but also poses security risks due to the potential leakage of sensitive data. On the other hand, existing data acquisition terminals often rigidly support specific temperature sensor models and fixed wiring methods, lacking automatic sensor model identification and hardware adaptation capabilities. When the sensor model changes or the wiring method is adjusted in the field, it is difficult to promptly verify the consistency of the sensor model, wiring status, and configuration, which can easily lead to mismatched acquisition parameters, distorted measurement data, or even complete failure to acquire data. Summary of the Invention

[0004] The purpose of this application is to provide a temperature monitoring system and method to overcome the shortcomings of traditional wireless temperature monitoring systems, such as lack of independent processing capabilities, strong dependence on public networks, difficulty in continuous monitoring and early warning when the network is down, and insufficient adaptation and configuration verification capabilities for different types of sensors, which easily lead to measurement errors.

[0005] In a first aspect, this application proposes a temperature monitoring system, the system comprising: a sensing layer and an edge server;

[0006] The sensing layer includes at least one acquisition terminal, which includes a main control unit, a conversion unit, a switching component, and multiple resistor networks.

[0007] One end of the switching assembly is connected to a temperature sensor, and the other end is connected to each of the resistor networks. When the temperature sensors are of different types, the on / off state of the switching assembly is different, so that the temperature sensor can selectively connect to the corresponding resistor network.

[0008] The main control unit is used to determine the type of the temperature sensor according to the on / off state of the switch assembly, and configure the conversion unit based on the type to output corresponding temperature detection data; wherein, the type includes the temperature sensor model and wiring method;

[0009] The edge server is used to perform time-series trend analysis on the temperature detection data, determine maintenance strategies based on the analysis results, generate early warning information when abnormal trends are detected, and determine cloud upload strategies.

[0010] In one embodiment, the switching assembly includes:

[0011] A control component, connected in series between the temperature sensor and each of the resistor networks, is used to selectively connect the temperature sensor to the corresponding resistor network by physically switching on and off.

[0012] A digital feedback component, connected to the main control unit, is used to provide the main control unit with a logic level representing the on / off state.

[0013] In one embodiment, the main control unit determines the type of the temperature sensor based on the on / off state of the switching assembly, and configures the conversion unit based on the type, including:

[0014] The main control unit reads the logic level corresponding to the on / off state and the correction flag pre-stored in the conversion unit;

[0015] Logical operations are performed on the logic level and the correction flag, and the type of temperature sensor is determined based on the operation result; the register of the conversion unit is configured according to the type; and the algorithm coefficient is obtained by querying a preset table according to the type. The equivalent resistance value output by the conversion unit is converted according to the algorithm coefficient to obtain the corresponding temperature detection data.

[0016] In one embodiment, the main control unit is further configured to, when the temperature detection data output by the conversion unit exceeds the allowable range, change the current setting parameters according to a preset rule, and reconfigure the register of the conversion unit based on the changed setting parameters, so that the conversion unit outputs new temperature detection data;

[0017] The main control unit is also used to output diagnostic information indicating that the switching component does not match the type when the new temperature detection data is within the allowable range.

[0018] In one embodiment, the main control unit is further configured to calculate the temperature change rate of the current acquisition cycle and the previous acquisition cycle based on the temperature detection data, and the duration of the current acquisition cycle and the previous acquisition cycle is a preset duration.

[0019] If the temperature change rate is less than a preset threshold, the duration of the next acquisition cycle is maintained at the preset duration.

[0020] If the temperature change rate is greater than a preset threshold, an early warning message is generated and the preset duration is shortened by a preset step size until the temperature change rate is less than the preset threshold for a preset number of consecutive times, at which point the preset duration is reused.

[0021] In one embodiment, the edge server includes:

[0022] The data access module is used to connect to each of the acquisition terminals and acquire the temperature detection data of each of the acquisition terminals.

[0023] The analysis module is used to perform time-series trend analysis on the temperature detection data, determine maintenance strategies based on the analysis results, and generate early warning information when abnormal trends are detected.

[0024] The edge collaboration module is used to determine the cloud upload strategy based on the preset filtering rule engine and analysis results;

[0025] The system also includes a cloud platform for receiving collaborative data uploaded by the edge server and providing remote management and control services.

[0026] In one embodiment, the analysis module performs time-series trend analysis on each of the temperature detection data, determines a maintenance strategy based on the analysis results, and generates early warning information when an abnormal trend is detected, including:

[0027] The analysis module performs sliding window analysis on each of the temperature detection data, and calculates the temperature change slope and fluctuation variance within the sliding window.

[0028] If the slope of the temperature change is greater than a preset temperature rise rate threshold, the trend is considered abnormal and an early warning message is generated. At this time, the maintenance strategy includes checking the load status and the operation of the cooling fan.

[0029] If the fluctuation variance is greater than a preset stability threshold and the drift of the average temperature within the sliding window is less than a preset drift, an abnormal trend is considered and an early warning message is generated. At this time, the maintenance strategy includes detecting loose wiring terminals, line contact conditions, and monitoring the electromagnetic interference of the environment.

[0030] In one embodiment, the filtering rule engine includes an alarm mode, an aggregation mode, and a full mode; the edge server also includes a data upload module and a storage module.

[0031] The analysis module is also used to clean or aggregate the temperature detection data based on a preset strategy to obtain key data characterizing temperature detection anomalies; and to encrypt the key data and transmit it to the cloud platform through the data upload module.

[0032] The edge collaboration module determines the cloud upload strategy based on a preset filtering rule engine and analysis results, including:

[0033] When the filtering rule engine includes an alarm mode, the cloud upload strategy includes uploading each temperature detection data and analysis result to the cloud platform only when an abnormal trend occurs; otherwise, storing them in the storage module.

[0034] When the filtering rule engine includes an aggregation mode, the cloud upload strategy includes storing each of the temperature detection data and analysis results to the storage module, calculating the statistical value of each of the temperature detection data according to a preset time window, and uploading the statistical value to the cloud platform; wherein, the statistical value includes at least one of the maximum value, minimum value, and average value;

[0035] When the filtering rule engine includes a full mode, the cloud upload strategy includes uploading each of the temperature detection data and analysis results to the cloud platform in real time.

[0036] In one embodiment, the edge server further includes:

[0037] The visualization management module is used to provide an operation interface for real-time display of the temperature detection data and analysis results; the operation interface is also used to receive management commands input by the user.

[0038] The analysis module is also used to parse the management instructions and cache the parsed management instructions when each of the acquisition terminals is in sleep mode;

[0039] The main control unit is also used to initiate a query operation to the edge server during the communication window period after being woken up, and update its register parameters based on the parsed management instructions obtained from the query.

[0040] Secondly, this application proposes a temperature monitoring method applicable to the temperature monitoring system described in any one of the first aspects; the method includes:

[0041] A sensing layer is used to acquire temperature detection data from a temperature sensor. The sensing layer includes at least one acquisition terminal, which comprises a main control unit, a conversion unit, a switching assembly, and multiple resistor networks. One end of the switching assembly is connected to the temperature sensor, and the other end is connected to each of the resistor networks. The switching assembly's on / off state varies depending on the type of temperature sensor, allowing the temperature sensor to selectively connect to the corresponding resistor network. The main control unit determines the type of temperature sensor based on the on / off state of the switching assembly and configures the conversion unit accordingly to output the corresponding temperature detection data. The type includes the temperature sensor model and wiring method.

[0042] An edge server is used to perform time-series trend analysis on the temperature detection data. Based on the analysis results, a maintenance strategy is determined, and when an abnormal trend is detected, an early warning message is generated, as well as a cloud upload strategy is determined.

[0043] The above-mentioned temperature monitoring system and method have at least the following advantages:

[0044] The temperature monitoring system of this application includes a sensing layer and an edge server. By setting a switchable resistor network in the acquisition terminal of the sensing layer, and using a switching component to selectively connect the sensor to the resistor network, the main control unit can automatically identify the temperature sensor type based on the on / off state of the switch and configure the conversion unit register accordingly. This enables adaptive acquisition and configuration for different sensor models, reducing measurement errors caused by sensor type mismatch. Meanwhile, the edge server performs local time-series trend analysis on the temperature detection data and generates early warning information when abnormal trends are detected. It can maintain the continuity of monitoring and alarms when the external network is interrupted, and achieves on-demand cloud upload by determining the cloud upload strategy. This reduces bandwidth consumption, lowers operating costs, and improves the security of sensitive data within the local area network. It reduces dependence on the cloud while improving reliability and maintainability in complex environments. Attached Figure Description

[0045] Figure 1 This is a block diagram of a temperature monitoring system in one embodiment;

[0046] Figure 2 This is a structural block diagram of the acquisition terminal in one embodiment;

[0047] Figure 3 This is a wiring diagram of the switching assembly and the resistor network in one embodiment;

[0048] Figure 4 This is a schematic diagram of the workflow of the data acquisition terminal in one embodiment;

[0049] Figure 5Here is a block diagram of the temperature monitoring system in another embodiment;

[0050] Figure 6 This is a schematic diagram of the working process of a temperature monitoring system in one embodiment;

[0051] Figure 7 This is a flowchart illustrating a temperature monitoring method in one embodiment. Detailed Implementation

[0052] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0053] Some exemplary embodiments of this application have been described for illustrative purposes. It should be understood that this application may be implemented in other ways not specifically shown in the accompanying drawings.

[0054] Please see Figure 1 and Figure 2 In one exemplary embodiment, this application provides a temperature monitoring system, including: a sensing layer and an edge server, wherein the sensing layer includes at least one acquisition terminal, and the acquisition terminal includes a main control unit, a conversion unit, a switching component and multiple resistor networks.

[0055] One end of the switching assembly is connected to a temperature sensor, and the other end is connected to various resistor networks. The switching assembly has different on / off states depending on the type of temperature sensor, so that the temperature sensor can selectively connect to the corresponding resistor network.

[0056] Specifically, different types of temperature sensors refer to different model numbers and / or wiring methods. For example, in this embodiment, the temperature sensor models are PT100 and PT1000, and the wiring methods are two-wire and three-wire, respectively, which would be two-wire PT100, two-wire PT1000, three-wire PT100, and three-wire PT1000.

[0057] Optionally, the switching assembly may employ a physical DIP switch, jumper, or conductive connector, wherein the conductive connector includes pads, 0-ohm resistors, or solder.

[0058] Furthermore, the switching assembly is also connected to the input / output ports of the main control unit to transmit the logic level corresponding to the on / off state to the main control unit.

[0059] The main control unit is used to determine the type of temperature sensor based on the on / off state of the switching component, and configure the register of the conversion unit based on the type so that the conversion unit outputs the corresponding temperature detection data.

[0060] Specifically, the main control unit in this embodiment uses a low-power SoC chip with integrated wireless communication functionality, which internally includes non-volatile memory (NVS). For example, the chip used in this embodiment is the ESP8684, which is based on a RISC-V 32-bit single-core processor architecture, integrates a 2.4 GHz Wi-Fi wireless communication module, and internally packages 4MB of SPI Flash, with a specific area designated as a non-volatile memory (NVS) partition for storing critical configurations and data. Its stored content includes device ID, network configuration, acquisition time interval, sensor calibration parameters, power mode settings, and historical data cache. NVS storage ensures that the device can retain its previous configuration after a power outage and restart, enabling immediate use upon power-on. Furthermore, when wireless communication is temporarily interrupted, a certain amount of temperature measurement data can be temporarily stored in the NVS and automatically retransmitted after communication is restored, enhancing data reliability. In addition, updates to configuration parameters are also written to the NVS so that newly issued parameters become permanent.

[0061] The conversion unit uses a platinum resistance thermometer (RTD), which is widely used in industrial temperature measurement due to its high accuracy and stability. For example, in this embodiment, the RTD is the Maxim Integrated MAX31865, a dedicated RTD digital conversion chip that integrates an ADC, fault detection, and an SPI interface, and is connected to the main control unit via the SPI bus.

[0062] Optionally, the switching component includes a control component and a digital feedback component.

[0063] A control component, connected in series between the temperature sensor and each resistor network, is used to selectively connect the temperature sensor to the corresponding resistor network by physically switching on and off.

[0064] The digital feedback component, connected to the main control unit, is used to provide the main control unit with logic levels that characterize the on / off state.

[0065] Please see Figure 3 For example, the connection relationship between the switching component and each resistor network is illustrated by taking a switching component that uses a physical DIP switch as an example.

[0066] like Figure 3 As shown, SW2 is a switching assembly of a group of 9 DIP switches, each of which is labeled S1-S9. When S1 is closed, pins 1 and 18 of SW2 are connected. U4 is a MAX31865.

[0067] S1-S7 constitute the control component of this application embodiment, and S8-S9 constitute the digital feedback component. S1-S7 are connected in series between the key analog pins and terminals of U4, wherein the key analog pins include BIAS, REFIN+, RTDIN+, RTDIN-, and ISENSOR. By closing and opening the physical switches, S1 to S7 can physically change the circuit topology: on the one hand, switching the reference resistor connected to the reference circuit; on the other hand, switching the bypass path of the input terminal to short-circuit the compensation circuit in two-wire mode, or to connect the compensation circuit in three-wire mode. S8-S9 are connected to the main control unit, wherein the level state of S8 is specifically used to identify the sensor type (Type ID), and the level state of S9 is specifically used to identify the wiring method (Wire ID). Exemplarily, S8 and S9 are connected to the GPIO18 pin and GPIO17 pin of ESP8684 respectively through pull-up resistors or pull-down resistors.

[0068] Specifically, the first precision resistor R15 has a resistance of 430Ω, and the second precision resistor R16 has a resistance of 4.3KΩ. These two precision resistors form two resistor networks. One end of the first precision resistor R15 is connected to pin 2 of SW2, and the other end is connected to pin 1 of U4. One end of the second precision resistor R16 is connected to pin 1 of SW2, and the other end is connected to pin 2 of U4. Pins 17 and 18 of SW2 are shorted and then connected to pins 1 and 4 of U4. Pin 1 (BIAS) of U4 is the bias port, and pin 2 (REFIN+) of U4 is the positive reference input. Shorting these two pins unifies the bias output and the positive reference input onto the same reference node. Thus, when the temperature sensor model is PT100, by turning on S2, the first precision resistor R15 is selectively connected to match the range and resolution of U4; when the temperature sensor model is PT1000, by turning on S1, the second precision resistor R16 is selectively connected.

[0069] Furthermore, if the wiring method is two-wire, lead resistance compensation is not possible, and the force line (FORCE) and sampling line (RTDIN) of U4 need to be shorted. If the wiring method is three-wire, the MAX31865 uses the FORCE2 sampling channel, and the voltage between FORCE+ and RTDIN+ is obtained from RTDIN+ and RTDIN... Differential cancellation is performed in the voltage.

[0070] Furthermore, Figure 3 Between SW2 and U4, there are also first capacitor C9 and second capacitor C10, which are used as ground bypass capacitors for input noise reduction and bandwidth limitation of RTDIN+, in order to improve anti-interference and reading stability.

[0071] For example, Table 1 provides a configuration table for different types of DIP switches. This table clearly defines the on / off states (i.e., ON and OFF in Table 1) that each DIP switch from S1 to S9 should be in for four typical types (two-wire PT100, two-wire PT1000, three-wire PT100, and three-wire PT1000). For example, when configuring to "two-wire PT1000" mode, the user needs to set S1, S3, S5, S7, and S9 to ON, and the rest to OFF. At this time, the hardware circuit physically forms the sampling loop of PT1000, and GPIO18 reads a low level (hypothetically defined), and GPIO7 reads a low level (indicating two-wire identification), thereby achieving synchronous unification of analog characteristics and digital identification.

[0072] Table 1. Configuration Table for Different Types of Downward DIP Switches

[0073] S1 S2 S3 S4 S5 S6 S7 S8 S9 type OFF ON ON OFF ON ON OFF ON ON Two-line PT100 ON OFF ON OFF ON OFF ON OFF ON Two-line PT1000 OFF ON OFF ON OFF ON OFF ON OFF Three-line PT100 ON OFF OFF ON OFF OFF ON OFF OFF Three-line PT1000

[0074] Optionally, the main control unit determines the type of temperature sensor based on the on / off state of the switching assembly, and configures the registers of the conversion unit based on the type, including:

[0075] The main control unit reads the logic level corresponding to the on / off state and the correction flag pre-stored in the conversion unit; performs logical operations on the logic level and correction flag, and determines the type of temperature sensor based on the operation result; configures the register of the conversion unit according to the type; and retrieves the algorithm coefficient from a preset table according to the type, and converts the equivalent resistance value output by the conversion unit according to the algorithm coefficient to obtain the corresponding temperature detection data.

[0076] Please see Figure 4 Specifically, before each measurement is initiated by the conversion unit, the main control unit first scans the physical level status of the digital feedback component and temporarily stores the reading results as physical type variables and physical wiring variables. Subsequently, the main control unit calls the NVS interface to read the pre-stored software correction flags in the storage area. These software correction flags include a type inversion flag and a wiring inversion flag. For example, these two flag bits are set to false (0) by default at the factory.

[0077] Further, the main control unit performs logical operations and judgments. The sensor model (Final_Type) is the XOR result of the physical type variable and the type inversion flag; the wire type (Final_Wire) is the XOR result of the physical wire type variable and the wire inversion flag. After completing the logical operations, the main control unit writes the results to the conversion unit's configuration register via the SPI bus. If the final wire type is determined to be three-wire, the corresponding bit in the configuration register (e.g., bit D4) is set to enable three-wire mode, and the bias voltage bit (VBIAS, e.g., bit D7) is set to 1 to provide the excitation voltage, thereby initiating the measurement. Finally, the main control unit retrieves the algorithm coefficients from a preset table based on the sensor type and loads these coefficients to complete the entire adaptive configuration process. The collected temperature detection data is then packaged and reported to the edge server.

[0078] By adopting the above scheme, a switchable resistor network is set in the acquisition terminal, and the sensor and the resistor network are selectively connected by the switching component. The main control unit can automatically identify the temperature sensor type based on the on / off state of the switch and configure the conversion unit register accordingly, thereby realizing adaptive acquisition of different sensor models and reducing measurement errors caused by sensor type mismatch.

[0079] Furthermore, this application allows users to remotely reverse the logic and correct configuration errors by sending instructions from the cloud to modify the NVS mask when the physical switch is incorrectly switched and the site cannot be reached. Based on the final logic obtained from the calculation, the main control unit dynamically rewrites the register of the conversion unit and loads the corresponding algorithm coefficients. For example, assuming that the PT1000 sensor is used on site, the DIP switch S8 should be switched to the high level. If S8 is stuck in the low level due to a fault, the maintenance personnel do not need to replace the equipment immediately. They only need to send instructions from the cloud to set the type inversion flag in the NVS to true (1). At this time, the logic operation result will be automatically corrected to the PT1000 mode, and the equipment can be restored to normal operation.

[0080] Optionally, the main control unit is also used to change the current setting parameters according to preset rules when the temperature detection data output by the conversion unit exceeds the allowable range, and to reconfigure the register of the conversion unit based on the changed setting parameters so that the conversion unit outputs new temperature detection data.

[0081] The main control unit is also used to output diagnostic information indicating that the switching components are not of the correct type when new temperature detection data is within the allowable range.

[0082] Specifically, when the temperature value calculated from the collected raw data exceeds the physically reasonable range, such as >800℃ or <-200℃, the main control unit will not simply report a sensor fault. Instead, it will initiate a virtual trial calculation diagnosis. The main control unit temporarily flips the current sensor type parameters in memory, for example, switching from the PT100 parameter set to the PT1000 parameter set, and performs a second calculation on the same raw data. If the result of the second calculation falls within a reasonable temperature range, such as -40℃ to +85℃, the current fault is determined to be "the DIP switch setting is incompatible with the actual sensor," and specific diagnostic prompts are generated and reported to guide the user to check the hardware settings, rather than blindly replacing the sensor.

[0083] Optionally, the main control unit is also used to calculate the temperature change rate of the current acquisition cycle and the previous acquisition cycle based on the temperature detection data, and the duration of the current acquisition cycle and the previous acquisition cycle is a preset duration; if the temperature change rate is less than a preset threshold, maintain the duration of the next acquisition cycle as the preset duration; if the temperature change rate is greater than the preset threshold, generate an early warning message and shorten the preset duration according to a preset step size until the temperature change rate is less than the preset threshold for a preset number of consecutive times, and then re-adopt the preset duration.

[0084] Specifically, during each data acquisition, the main control unit calculates the rate of temperature change between the current temperature and the previous acquisition cycle. If the rate of temperature change is lower than a preset threshold, the current preset duration is used as the duration of the next acquisition cycle. If the rate of temperature change exceeds the threshold, it indicates a sudden temperature change, and an early warning message is immediately generated and reported first. The system automatically switches to emergency tracking mode, forcibly shortening the sampling interval. In subsequent continuous sampling, the temperature change trajectory is recorded until the rate of temperature change returns to below the preset threshold after a preset number of consecutive samplings, indicating that the temperature has stabilized. At this point, the system automatically resumes the preset duration. This logic ensures that no critical temperature anomaly fluctuations are missed while maintaining extremely low average power consumption.

[0085] Optionally, the aforementioned data acquisition terminal may also include a power module.

[0086] The power supply module provides a stable operating voltage for the data acquisition terminal. The main control unit also monitors voltage data in real time and generates alarm information when the voltage data exceeds the allowable range.

[0087] Edge servers are used to perform time-series trend analysis on temperature detection data, determine maintenance strategies based on the analysis results, generate early warning information when abnormal trends are detected, and determine cloud upload strategies.

[0088] Specifically, the edge server is deployed within the local area network of the monitoring site, serving as a hub connecting the perception layer and the cloud. This server integrates an MQTT message broker service to handle high-concurrency data access requests.

[0089] At the physical deployment level, edge servers are preferably industrial-grade Raspberry Pi or x86 architecture industrial control computers, which are connected to the monitoring site's local area network via Ethernet interface and configured with static IP addresses to ensure stable operation as gateway devices.

[0090] Furthermore, the edge server includes: a data access module, an analysis module, and an edge collaboration module.

[0091] The data access module is used to connect to each acquisition terminal and obtain the temperature detection data from each acquisition terminal.

[0092] The analysis module is used to perform time-series trend analysis on various temperature detection data, determine maintenance strategies based on the analysis results, and generate early warning information when abnormal trends are detected.

[0093] The edge collaboration module is used to determine the cloud upload strategy based on the preset filtering rule engine and analysis results.

[0094] Specifically, the data access module connects wirelessly to a Wi-Fi local area network (LAN) and receives JSON-formatted data packets sent by the perception layer via the LAN. Furthermore, the edge server also includes a storage module; the analysis module parses the data packets and stores them in the storage module. The storage module employs a hybrid storage architecture, including a time-series database for storing high-frequency sampling data and a relational database for storing device metadata and user permissions.

[0095] Optionally, the analysis module performs time-series trend analysis on each temperature detection data, determines maintenance strategies based on the analysis results, and generates early warning information when abnormal trends are detected, including:

[0096] The analysis module performs sliding window analysis on each temperature detection data, calculating the slope of temperature change and the variance of fluctuation within the sliding window.

[0097] If the slope of temperature change is greater than the preset temperature rise rate threshold, the trend is considered abnormal and an early warning message is generated. At this time, the maintenance strategy includes checking the load status and the operation of the cooling fan.

[0098] If the variance of the fluctuation is greater than the preset stability threshold and the drift of the average temperature within the sliding window is less than the preset drift, an abnormal trend is considered and an early warning message is generated. At this time, the maintenance strategy includes checking the looseness of the wiring terminals, the contact of the lines, and the electromagnetic interference of the monitoring environment.

[0099] Specifically, this application first maintains a fixed-length sliding window of length N (e.g., N=10 seconds) for each connected temperature sensor. Whenever new temperature sampling data T_now arrives, the sliding window queue is updated, and parallel analysis logic is initiated.

[0100] Regarding temperature rise rate monitoring, the analysis module calculates the linear regression slope or simple difference slope within the current window in real time, using the following formula:

[0101]

[0102] in, This indicates the current temperature value; This represents the temperature value at the start of the window, i.e., the temperature value N time steps ago. It represents the time interval, that is, the time difference between the current time and the starting time.

[0103] By presetting a temperature rise rate threshold (For example, 2℃ / min), the logic engine will compare the calculated Slope with the temperature rise rate threshold. If the Slope is greater than... Even if the current absolute temperature has not yet reached the upper limit for triggering an alarm (e.g., 80°C), an "abnormal temperature rise" warning signal will be generated immediately. This mechanism can effectively prompt maintenance personnel to check the equipment load status or the operation of the cooling fan in advance, thereby intervening in the early stages of a fault.

[0104] In terms of fluctuation stability monitoring, the analysis module assesses the dispersion of the data by calculating the standard deviation σ of the temperature data within the sliding window. The calculation formula is as follows:

[0105]

[0106] Where N represents the total number of temperature data points within the sliding window; This represents the i-th temperature measurement value within the sliding window; This represents the average temperature within the window.

[0107] The logic engine compares the calculated standard deviation σ with a preset stability threshold (e.g., 0.5). If σ exceeds this threshold, and the mean temperature μ does not drift significantly at the same time, the current state is determined to be "non-steady-state fluctuation". This specific fluctuation characteristic usually indicates that the temperature sensor's wiring terminals are loose, the wiring is not making good contact, or there is strong electromagnetic interference in the field environment. Based on this, the engine automatically generates corresponding maintenance suggestions, realizing intelligent diagnosis of non-functional faults.

[0108] Please see Figure 5Optionally, the temperature monitoring system described above may also include: a cloud platform.

[0109] The cloud platform connects to the edge server via a wide area network or the Internet to receive collaborative data uploaded by the edge server and provide remote management and control services.

[0110] Optionally, the edge server may also include a data upload module.

[0111] The analysis module is also used to clean or aggregate the temperature detection data based on a preset strategy to obtain key data characterizing temperature detection anomalies; and then encrypt the key data and transmit it to the cloud platform through the data upload module.

[0112] Specifically, the edge server periodically receives temperature detection data uploaded by each acquisition terminal. Typically, the temperature detection data includes device identifier, sampling timestamp, temperature value, and sensor type. The analysis module cleans the temperature detection data to remove duplicates, handle missing data, and process anomalies. The cleaned data is then aligned according to timestamps to form a unified sequence of detection data.

[0113] Furthermore, the analysis module aggregates the various detection data. When the analysis module identifies anomalies, it generates an alarm event log. These anomalies include at least one of the following: temperature exceeding limits, abnormal heating rate, continuous drift, abnormal fluctuation, or temperature sensor offline. The analysis module also performs hourly statistical analysis on the temperature data corresponding to each alarm event to generate hourly statistical values. The alarm event logs and their corresponding hourly statistical values ​​constitute key data.

[0114] Furthermore, the data upload module encrypts the aforementioned key data before uploading it to the cloud platform. The encryption method can be either symmetric or asymmetric encryption.

[0115] Using the above solution, the edge server completes the cleaning and aggregation of temperature detection data locally, and only retains key data related to anomalies, such as event-level and statistical information, for encrypted cloud transmission. This ensures independent monitoring and rapid early warning within the local area network, while reducing the bandwidth and cost overhead of uploading all data to the cloud and minimizing the risk of sensitive data being transmitted outside the network.

[0116] Optionally, when the filtering rule engine includes alarm mode, aggregation mode, and full mode, the edge collaboration module determines the cloud upload strategy based on the preset filtering rule engine and analysis results, including:

[0117] When the filtering rule engine includes an alarm mode, the cloud upload strategy includes uploading each temperature detection data and analysis result to the cloud platform only when an abnormal trend occurs; otherwise, it is stored in the storage module.

[0118] When the filtering rule engine includes an aggregation mode, the cloud upload strategy includes storing each temperature detection data and analysis result to the storage module, calculating the statistical value of each temperature detection data according to a preset time window, and uploading the statistical value to the cloud platform; wherein, the statistical value includes at least one of the maximum value, minimum value, and average value.

[0119] When the filtering rule engine includes a full mode, the cloud upload strategy includes uploading various temperature detection data and analysis results to the cloud platform in real time.

[0120] Specifically, the edge collaboration module executes edge computing strategies to determine cloud upload strategies. Among these, alarm mode stores normal data locally, triggering uploads only in abnormal situations. Aggregation mode stores raw data locally, uploading statistical values ​​within a preset time window. Full upload mode transmits all raw data in real time, suitable for debugging or high-risk periods. Furthermore, the filtering rule engine also includes a custom mode, allowing users to define composite rules via scripts, such as enabling full uploads during specific time periods and aggregated uploads at other times, or dynamically adjusting the upload frequency based on temperature change rates.

[0121] By adopting the above solution, the temperature is stored locally when it is within the normal range and uploaded only when there is an anomaly. This process ensures the real-time monitoring of the site while effectively reducing the cost of cloud data storage and network transmission load.

[0122] Optionally, the edge server may also include a visual management module.

[0123] The visualization management module provides an interface for real-time display of temperature detection data and analysis results.

[0124] Specifically, after receiving temperature detection data, the edge server initiates a parallel processing mechanism: on the one hand, it persists the data to a local database, and on the other hand, it pushes the data to the front-end screen in real time for dynamic refresh via a WebSocket channel, thereby achieving a millisecond-level local visualization closed loop.

[0125] At the software technology stack level, the edge servers are built on a Linux operating system (such as Ubuntu Server) and deploy application service groups using Docker containerization technology. This service group specifically includes:

[0126] MQTT message broker services (such as EMQX) listen on specific ports to receive collection information topics published by the perception layer terminals.

[0127] Time-series databases (such as InfluxDB) are used to store raw sampled temperature and voltage data with high throughput;

[0128] Relational databases (such as MySQL) are used to persistently store user information, device metadata, operation logs, and the relationships between devices and users;

[0129] The business logic service, developed based on the Python FastAPI framework, is responsible for handling HTTP requests, executing edge computing rules, and pushing messages in real time via the WebSocket protocol. The front end can interact with it through the REST API. The front end service uses Nginx to host web pages developed based on React, providing large-screen monitoring and backend management interfaces.

[0130] Optionally, management commands can also be issued through a visual management module or a cloud platform.

[0131] The user interface is also used to receive management commands input by the user.

[0132] The analysis module is also used to parse management commands and cache the parsed management commands when each acquisition terminal is in sleep mode.

[0133] The main control unit is also used to initiate a query operation to the edge server during the communication window after being woken up, and update its register parameters based on the parsed management instructions obtained from the query.

[0134] Specifically, users can issue management commands through the local front-end operation interface or the cloud platform. These management commands are first parsed by the edge server and synchronously updated to the NVS of the acquisition terminal to achieve configuration persistence.

[0135] Please see Figure 6 The following is combined Figure 6 The working process of the temperature monitoring system in this application is described.

[0136] Each data acquisition terminal in the perception layer is connected to the edge server via a local area network, and the edge server is connected to the cloud platform via a wide area network or the Internet.

[0137] The data acquisition terminal reports the temperature detection data to the edge server in JSON format. After receiving the temperature detection data, the edge server initiates a parallel processing mechanism. On the one hand, it persists the data to a local database, and on the other hand, it pushes the data to the front-end dashboard in real time for dynamic updates via a WebSocket channel, thus achieving a millisecond-level local visualization closed loop.

[0138] Furthermore, the edge server analyzes the temperature detection data and executes preset filtering rules. If the temperature is normal, it only stores the data locally without uploading it to the cloud platform. If the temperature exceeds the limit, it generates an alarm message and immediately uploads the alarm event to the cloud platform. In aggregation mode, the edge server also calculates temperature statistics within a preset time window, such as calculating the hourly average value, and uploads the hourly average value to the cloud platform.

[0139] Furthermore, users can also issue management commands to the edge server via the user interface or cloud platform for reverse control. These management commands are first parsed by the edge server and then synchronously updated to the NVS of the data acquisition terminal, thus achieving configuration persistence.

[0140] The aforementioned temperature monitoring system, by incorporating a switchable resistor network within the data acquisition terminal and using a switching component to selectively connect the sensor to the resistor network, allows the main control unit to automatically identify the temperature sensor type based on the switch's on / off state and configure the conversion unit register accordingly. This enables adaptive data acquisition for different sensor models, reducing measurement errors caused by sensor type incompatibility. The introduced edge server enables local closed-loop processing and storage of temperature data, reducing reliance on external networks and improving system real-time performance and security. This application significantly enhances the data acquisition terminal's compatibility with different sensor models through a hardware-software integrated adaptive configuration technology, reducing hardware maintenance costs. Furthermore, edge intelligent analysis enables proactive assessment of temperature change trends, allowing for early detection of potential problems and improving operational efficiency.

[0141] Based on the same inventive concept, this application also provides a temperature monitoring method. This method is applicable to the temperature monitoring system described above. The solution provided by this method is similar to the solution described in the system described above. Therefore, the specific limitations of one or more device embodiments provided below can be found in the system limitations above, and will not be repeated here.

[0142] Please see Figure 7 In one exemplary embodiment, this application provides a temperature monitoring method, specifically including the following steps:

[0143] Step 702: Acquire temperature detection data from the temperature sensor using the sensing layer. The sensing layer includes at least one acquisition terminal, which comprises a main control unit, a conversion unit, a switching assembly, and multiple resistor networks. One end of the switching assembly is connected to the temperature sensor, and the other end is connected to each resistor network. The on / off state of the switching assembly varies depending on the type of temperature sensor, allowing the temperature sensor to selectively connect to the corresponding resistor network. The main control unit determines the type of temperature sensor based on the on / off state of the switching assembly and configures the conversion unit accordingly to output the corresponding temperature detection data. The type includes the temperature sensor model and wiring method.

[0144] Step 704: Use an edge server to perform time-series trend analysis on the temperature detection data, determine the maintenance strategy based on the analysis results, generate early warning information when abnormal trends are detected, and determine the cloud upload strategy.

[0145] Optionally, the above temperature monitoring method also includes: using a visual management module to provide an operation interface to display the temperature detection data and analysis results in real time.

[0146] Optionally, the above temperature monitoring method also includes: issuing management commands through a visual management module or cloud platform. These management commands are first parsed by the edge server and synchronously updated to the NVS of the acquisition terminal to achieve configuration persistence.

[0147] The aforementioned temperature monitoring method, by setting a switchable resistor network within the acquisition terminal and using a switching component to selectively connect the sensor to the resistor network, allows the main control unit to automatically identify the temperature sensor type based on the switch's on / off state and configure the conversion unit register accordingly. This enables adaptive acquisition of different sensor models, reducing measurement errors caused by sensor type incompatibility. The introduced edge server enables local closed-loop processing and storage of temperature data, reducing reliance on external networks and improving system real-time performance and security. This application significantly enhances the acquisition terminal's compatibility with different sensor models through a hardware-software integrated adaptive configuration technology, reducing hardware maintenance costs. Furthermore, edge intelligent analysis enables proactive assessment of temperature change trends, allowing for early detection of potential problems and improving operational efficiency.

[0148] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A temperature monitoring system, characterized in that, The system includes: a perception layer and an edge server; The sensing layer includes at least one acquisition terminal, which includes a main control unit, a conversion unit, a switching component, and multiple resistor networks. One end of the switching assembly is connected to a temperature sensor, and the other end is connected to each of the resistor networks. The switching assembly operates in different on / off states depending on the type of temperature sensor, allowing the temperature sensor to selectively connect to the corresponding resistor network. The switching assembly includes: A control component, connected in series between the temperature sensor and each of the resistor networks, is used to selectively connect the temperature sensor to the corresponding resistor network by physically switching on and off. A digital feedback component, connected to the main control unit, is used to provide the main control unit with a logic level representing the on / off state; The main control unit is used to determine the type of the temperature sensor based on the on / off state of the switch assembly, and configure the conversion unit based on the type to output corresponding temperature detection data. This includes: the main control unit reading the logic level corresponding to the on / off state and a pre-stored correction flag in the conversion unit; performing logical operations on the logic level and the correction flag, and determining the type of the temperature sensor based on the operation result; configuring the register of the conversion unit according to the type; and querying an algorithm coefficient in a preset table based on the type, converting the equivalent resistance value output by the conversion unit according to the algorithm coefficient to obtain the corresponding temperature detection data; wherein the type includes the temperature sensor model and wiring method. The edge server is used to perform time-series trend analysis on the temperature detection data, determine maintenance strategies based on the analysis results, generate early warning information when abnormal trends are detected, and determine cloud upload strategies.

2. The temperature monitoring system according to claim 1, characterized in that: The main control unit is also used to change the current setting parameters according to a preset rule when the temperature detection data output by the conversion unit exceeds the allowable range, and to reconfigure the register of the conversion unit based on the changed setting parameters so that the conversion unit outputs new temperature detection data. The main control unit is also used to output diagnostic information indicating that the switching component does not match the type when the new temperature detection data is within the allowable range.

3. The temperature monitoring system according to claim 1, characterized in that: The main control unit is also used to calculate the temperature change rate of the current acquisition cycle and the previous acquisition cycle based on the temperature detection data, and the duration of the current acquisition cycle and the previous acquisition cycle is a preset duration. If the temperature change rate is less than a preset threshold, the duration of the next acquisition cycle is maintained at the preset duration. If the temperature change rate is greater than a preset threshold, an early warning message is generated and the preset duration is shortened by a preset step size until the temperature change rate is less than the preset threshold for a preset number of consecutive times, at which point the preset duration is reused.

4. The temperature monitoring system according to claim 1, characterized in that, The edge server includes: The data access module is used to connect to each of the acquisition terminals and acquire the temperature detection data of each of the acquisition terminals. The analysis module is used to perform time-series trend analysis on the temperature detection data, determine maintenance strategies based on the analysis results, and generate early warning information when abnormal trends are detected. The edge collaboration module is used to determine the cloud upload strategy based on the preset filtering rule engine and analysis results; The system also includes a cloud platform for receiving collaborative data uploaded by the edge server and providing remote management and control services.

5. The temperature monitoring system according to claim 4, characterized in that, The analysis module performs time-series trend analysis on each of the temperature detection data, determines maintenance strategies based on the analysis results, and generates early warning information when abnormal trends are detected, including: The analysis module performs sliding window analysis on each of the temperature detection data, and calculates the temperature change slope and fluctuation variance within the sliding window. If the slope of the temperature change is greater than a preset temperature rise rate threshold, the trend is considered abnormal and an early warning message is generated. At this time, the maintenance strategy includes checking the load status and the operation of the cooling fan. If the fluctuation variance is greater than a preset stability threshold and the drift of the average temperature within the sliding window is less than a preset drift, an abnormal trend is considered and an early warning message is generated. At this time, the maintenance strategy includes detecting loose wiring terminals, line contact conditions, and monitoring the electromagnetic interference of the environment.

6. The temperature monitoring system according to claim 4, characterized in that, The filtering rule engine includes alarm mode, aggregation mode and full mode; the edge server also includes a data upload module and a storage module. The analysis module is also used to clean or aggregate the temperature detection data based on a preset strategy to obtain key data characterizing temperature detection anomalies; and to encrypt the key data and transmit it to the cloud platform through the data upload module. The edge collaboration module determines the cloud upload strategy based on a preset filtering rule engine and analysis results, including: When the filtering rule engine includes an alarm mode, the cloud upload strategy includes uploading each temperature detection data and analysis result to the cloud platform only when an abnormal trend occurs; otherwise, storing them in the storage module. When the filtering rule engine includes an aggregation mode, the cloud upload strategy includes storing each of the temperature detection data and analysis results to the storage module, calculating the statistical value of each of the temperature detection data according to a preset time window, and uploading the statistical value to the cloud platform; wherein, the statistical value includes at least one of the maximum value, minimum value, and average value; When the filtering rule engine includes a full mode, the cloud upload strategy includes uploading each of the temperature detection data and analysis results to the cloud platform in real time.

7. The temperature monitoring system according to claim 4, characterized in that, The edge server also includes: The visualization management module is used to provide an interface for real-time display of the temperature detection data and analysis results. The user interface is also used to receive management instructions input by the user; The analysis module is also used to parse the management instructions and cache the parsed management instructions when each of the acquisition terminals is in sleep mode; The main control unit is also used to initiate a query operation to the edge server during the communication window period after being woken up, and update its register parameters based on the parsed management instructions obtained from the query.

8. A temperature monitoring method, characterized in that, The method is applicable to the temperature monitoring system according to any one of claims 1-7; the method includes: A sensing layer is used to acquire temperature detection data from a temperature sensor. The sensing layer includes at least one acquisition terminal, which comprises a main control unit, a conversion unit, a switching assembly, and multiple resistor networks. One end of the switching assembly is connected to the temperature sensor, and the other end is connected to each of the resistor networks. The switching assembly has different on / off states depending on the type of temperature sensor, allowing the temperature sensor to selectively connect to the corresponding resistor network. The switching assembly includes: a control component connected in series between the temperature sensor and each resistor network, used to selectively connect the temperature sensor to the corresponding resistor network through physical on / off switching; and a digital feedback component connected to the main control unit, used to provide the main control unit with a logic level representing the on / off state. The main control unit determines the type of the temperature sensor based on the on / off state of the switching assembly, and configures the conversion unit based on the type to output corresponding temperature detection data. This includes: the main control unit reading the logic level corresponding to the on / off state and a pre-stored correction flag in the conversion unit; performing logical operations on the logic level and the correction flag, and determining the type of the temperature sensor based on the operation result; configuring the register of the conversion unit according to the type; and querying an algorithm coefficient from a preset table based on the type, converting the equivalent resistance value output by the conversion unit according to the algorithm coefficient to obtain the corresponding temperature detection data; wherein, the type includes the temperature sensor model and wiring method. An edge server is used to perform time-series trend analysis on the temperature detection data. Based on the analysis results, a maintenance strategy is determined, and when an abnormal trend is detected, an early warning message is generated, as well as a cloud upload strategy is determined.

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