A multi-point temperature measurement monitoring method and system

Abstract

The present invention relates to the field of temperature measurement and monitoring technology, and in particular to a multi-point temperature measurement monitoring method and system, the method comprising: arranging multiple temperature sensors in a probe, establishing a data connection between the temperature sensor and a temperature monitoring terminal APP; the temperature monitoring terminal APP assigns a display button to each temperature sensor, and the temperature monitoring terminal APP stores an identification ID of each temperature sensor; establishing a data connection between the temperature monitoring terminal APP and a cloud server, the temperature monitoring terminal APP stores a voice processing model and a control instruction set, and the control instruction set stores multiple instructions for performing different tasks, and the temperature monitoring terminal APP analyzes the received voice signal through the voice processing model, and performs corresponding tasks according to the matched instructions. The present invention can collect and transmit temperature data of the temperature sensor in real time, analyze and process the temperature data, and realize the prediction and alarm of temperature trends.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature measurement and monitoring, and in particular to a multi-point temperature measurement and monitoring method and system. Background Art

[0002] In commercial and domestic food temperature measurement, a temperature probe needs to be inserted into the food to be processed to measure the temperature change inside. Existing temperature probes mainly use NTC or thermocouples. Multiple temperature sensors (NTC or thermocouples) are set in the temperature probe to collect the temperature of multiple measurement points inside the food. The temperature sensor is connected to the outside by wires.

[0003] As people's requirements for food quality increase, in order to meet the requirements for accurate temperature control of food in traditional cooking such as grilling, air frying, and smoking, a temperature measurement and monitoring system is needed that can automatically detect and monitor the temperature of multiple points inside the food in real time. At the same time, due to the high temperature environment during cooking, how to reduce the frequency of manual operation to query the temperature and reduce the probability of high temperature burns is also something that needs to be considered. Summary of the invention

[0004] The present invention connects the temperature probe via a wireless network, can collect and transmit the temperature data of each temperature sensor inside the probe in real time, analyze and process the temperature data, and make predictions and alarms in combination with a machine learning algorithm; and in combination with a natural language processing algorithm, voice-controlled temperature collection can be achieved, thereby reducing manual operations in high temperature environments and reducing the risk of burns.

[0005] The technical solution provided by the present invention is: a multi-point temperature measurement and monitoring method, the method comprising:

[0006] A plurality of temperature sensors are arranged in a linear manner in the probe;

[0007] Establish data connection between temperature sensor and temperature monitoring terminal APP;

[0008] The temperature monitoring terminal APP assigns a display button to each temperature sensor and receives display button operation information. The temperature monitoring terminal APP stores the identification ID of each temperature sensor, and each identification ID uniquely corresponds to a display button;

[0009] Establish a data connection between the temperature monitoring terminal APP and the cloud server. The temperature monitoring terminal APP forms a tuple with the temperature data and identification ID of the temperature sensor obtained, and sends the tuple to the cloud server.

[0010] The temperature monitoring terminal APP stores a voice processing model and a control instruction set, wherein the control instruction set stores a plurality of instructions for executing different tasks. The temperature monitoring terminal APP analyzes the received voice signal through the voice processing model, executes corresponding instructions according to the analysis results, and completes one or more of the temperature query task, the temperature prediction task, and the high temperature alarm task;

[0011] The temperature sensor is a thermocouple and / or a thermistor, and the number of the temperature sensors is no less than 3.

[0012] Preferably, the temperature monitoring terminal APP is divided into a three-layer architecture, namely, a data layer, an application layer and a presentation layer;

[0013] The data layer is used to store and manage the data of the temperature sensor and the preset high temperature alarm text information; the application layer is used to process the business logic, which includes data analysis and alarm mechanism; the application layer is provided with a temperature trend prediction model and a voice processing model;

[0014] The presentation layer is used to complete direct interaction tasks with the user, including real-time display of temperature, driving external voice equipment for voice broadcasting, and collecting operation information of display buttons; the operation information includes touch and press operations.

[0015] Preferably, the temperature monitoring terminal APP stores the temperature monitoring terminal's own ID information, the number of temperature sensors, the identification ID of each temperature sensor and the corresponding display button information through the data layer; the temperature monitoring terminal's own ID information, the number of temperature sensors, the identification ID of each temperature sensor and the corresponding display button information are loaded when the temperature monitoring terminal APP is initialized, so that the temperature sensor can work normally according to the preset configuration.

[0016] Preferably, the temperature monitoring terminal APP assigns a unique display button to each temperature sensor, and corresponds one to one with the identification ID of the temperature sensor. When the user operates the corresponding display button, the temperature monitoring terminal APP sends an instruction to query the temperature data of the corresponding temperature sensor.

[0017] Preferably, a unique identification ID is assigned to each temperature sensor through RFID technology, and the identification ID is stored in a cloud server and bound to a display button in the temperature monitoring terminal APP.

[0018] Preferably, a temperature monitoring module is stored in the APP application layer of the temperature monitoring terminal. The temperature monitoring module is used to periodically query and display the temperature information of each temperature sensor, and send the temperature information and the identification ID of the corresponding temperature sensor to the cloud service for storage. Users can remotely obtain the temperature sensor information and the corresponding temperature data by connecting to the cloud server. The temperature information can also be used for later data analysis;

[0019] A high-temperature alarm model and a temperature trend prediction model are set in the APP application layer of the temperature sensor. The high-temperature alarm model and the temperature trend prediction model are constructed through machine learning algorithms.

[0020] Preferably, the voice processing model is set in the application layer. The voice processing model uses natural language processing algorithms to identify and process voice signals, converts the input voice signals into text information, extracts keywords from the text information using keyword extraction algorithms, and matches the keywords with the instructions in the control instruction set using context understanding algorithms. If the match is successful, the corresponding instructions are executed.

[0021] Preferably, executing corresponding instructions according to the analysis results to complete one or more of the temperature query task, temperature prediction task, and high-temperature alarm task includes the following steps:

[0022] 1. If the instruction matched with the keyword in the instruction set is for the temperature query task, the following steps are taken:

[0023] Collect the real-time temperature data of multiple temperature sensors;

[0024] Send the temperature information and the identification ID of the corresponding temperature sensor to the display layer for display;

[0025] 2. If the instruction matched with the keyword in the instruction set is for the high-temperature alarm task, the following steps are taken:

[0026] Call the high-temperature alarm model;

[0027] Collect the real-time temperature data of multiple temperature sensors, add a collection timestamp to the temperature data, and then send the temperature data with the collection timestamp to the cloud server;

[0028] Execute the high-temperature alarm task through the high-temperature alarm model; specifically:

[0029] Compare the real-time temperature with the preset high-temperature threshold T0. If |T - T0| < M, generate a warning text message and send it to the display layer for display; where T is the real-time temperature, M is the error value, and M > 0;

[0030] Then, the warning text information is added with the information timestamp and sent to the cloud server. After the information timestamp and the collection timestamp are aligned in the cloud server, an alarm-temperature feature vector is generated. Multiple alarm-temperature feature vectors constitute the alarm feature set Q, which is convenient for later analysis.

[0031] Users remotely obtain alarm information through cloud services;

[0032] 3. If the instruction in the instruction set that matches the keyword is for the temperature prediction task, perform the following steps:

[0033] Call the trained temperature trend prediction model;

[0034] Collect temperature data from multiple temperature sensors, normalize the temperature data, and use the data as input variables of the temperature trend prediction model and input them into the temperature trend prediction model;

[0035] Output the alarm characteristic value at a future moment through the temperature trend prediction model ;

[0036] when When , it is predicted that a high temperature alarm will occur at a certain moment in the future, and the preset high temperature alarm text information is obtained from the data layer;

[0037] The high temperature warning text information is sent to the voice processing model, and the voice processing model generates voice information from the high temperature warning text information and sends it to the presentation layer, and the voice output of the high temperature warning information is performed through an external voice broadcasting device.

[0038] The present invention also provides a multi-point temperature measurement and monitoring system, wherein the system is used to execute the multi-point temperature measurement and monitoring method.

[0039] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the multi-point temperature measurement and monitoring method.

[0040] Beneficial effects of the present invention:

[0041] In the present invention, a voice processing model is built into the temperature monitoring terminal APP, allowing the user to query the temperature or perform other operations through voice commands, aiming to improve the convenience of operation; wherein, the control instruction set includes multiple instructions, each instruction is used to perform a different task, when the user issues a voice command, the APP can recognize the voice content and automatically execute the corresponding instruction to complete the task. This process realizes the intelligentization and management of temperature control, reduces the frequency of manual operation, improves user experience, makes operation more convenient, and ensures the accuracy and timeliness of temperature monitoring.

[0042] In the present invention, a timer is set in the temperature monitoring terminal APP (for example, every minute or every ten seconds) to periodically query the data of each temperature sensor. This function can be implemented through the timed task scheduling logic of the temperature monitoring terminal APP to display the temperature data of each sensor in real time, helping users to quickly identify any abnormal conditions; at the same time, the collected temperature data and identification ID are organized into a structured format, and then uploaded to the cloud server through a secure data transmission protocol (such as SSL / TLS) to facilitate data storage, retrieval and analysis.

[0043] In the present invention, the alarm characteristic value at a future moment is outputted through the temperature trend prediction model. ;when When the temperature reaches 400 °C, it is predicted that a high temperature alarm will occur at a certain moment in the future, and the preset high temperature alarm text information is obtained from the data layer; the voice processing model integrated in the temperature monitoring terminal APP is used to convert the high temperature warning text information into an audio signal that can be played through an external speaker.

[0044] If a high temperature condition is detected, a warning text message containing the specific temperature value, time, and possible impact or recommended actions will be generated immediately.

[0045] In the present invention, through the pre-trained high temperature alarm model, it is possible to determine whether there is a high temperature abnormality according to the input temperature data and generate an alarm message. The generated alarm message is first presented on the display layer so that the on-site personnel can take immediate action, and then it will be sent to the cloud server, and the information will be sent to the user through the cloud server;

[0046] An information timestamp is added to the alarm information. The cloud server combines the information timestamp with the previously aligned collection timestamp to form an "alarm-temperature feature vector". Multiple "alarm-temperature feature vectors" are aggregated into an alarm feature set Q in the cloud, which provides a basis for subsequent optimization of temperature control strategies or improvement of sensor layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0049] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0050] Please combine Figure 1 The present invention provides a multi-point temperature measurement and monitoring method, the specific steps are as follows:

[0051] No less than 3 temperature sensors are set in the probe, and the temperature sensors are arranged linearly in the probe; this helps to accurately measure the temperature changes at different locations. The temperature sensor and the temperature monitoring terminal APP are connected via a wireless network; the temperature monitoring terminal APP assigns display buttons to each temperature sensor and receives display button operation information. These buttons can exist in physical form around the device or as soft keys on the APP screen;

[0052] The temperature monitoring terminal APP stores the identification ID of each temperature sensor, and each identification ID uniquely corresponds to a display button; specifically: when a new temperature sensor is added to the system, the temperature monitoring terminal APP records the sensor through an automated registration process. This may include scanning the sensor's barcode or NFC tag, or automatically discovering and identifying new devices via Bluetooth. Once the sensor is identified, the system automatically assigns it a unique identification ID. The identification ID is used within the APP to track and manage sensor data and is associated with a display button. This allows users to access relevant data directly through buttons. When the user operates a button, the APP can respond quickly and display the correct temperature information.

[0053] The temperature monitoring terminal APP stores a voice processing model and a control instruction set. The control instruction set stores multiple instructions for performing different tasks. The temperature monitoring terminal APP analyzes the received voice signal through the voice processing model, executes corresponding instructions according to the analysis results, and completes one or more of the temperature query task, temperature prediction task and high temperature alarm task.

[0054] The temperature monitoring terminal APP is connected to the cloud server through a wireless network. The temperature monitoring terminal APP forms a tuple with the temperature data and identification ID of the temperature sensor obtained, and sends the tuple to the cloud server. The tuple can be expressed as , establish the corresponding relationship between the identification ID of the temperature sensor and the temperature data. For example, three temperature sensors are set in the probe, and the identification IDs of each temperature sensor are S01, S02, and S03, respectively. The corresponding temperature data are 10℃, 11℃, and 10℃, then the two tuples are , , .

[0055] In the temperature monitoring terminal APP, each button corresponds to a temperature sensor and is equipped with an intuitive icon or label. Each display button is associated with the identification ID of the corresponding temperature sensor. It can be configured in the background through programming languages ​​(such as Java for Android and Swift for iOS). Specifically, the buttons can be defined through XML layout files, and event listeners and processing logic can be added to these buttons in the Java code of Activity or Fragment. In the layout file of the application (such as activity_main.xml), add a Button control for each temperature sensor and assign it a unique ID.

[0056] In some preferred embodiments, a unique identification ID can be assigned to each temperature sensor through RFID technology. The identification ID is stored in a cloud server and bound to a display button in the temperature monitoring terminal APP. The sensor can be quickly identified and registered through an RFID reader, simplifying the initialization process.

[0057] In order to enable the temperature monitoring terminal APP to realize temperature monitoring, high temperature alarm and temperature trend prediction functions, the following solution is given in this embodiment:

[0058] By setting a timer inside the temperature monitoring terminal APP (for example, every minute or every ten seconds), the data of each temperature sensor is periodically queried. This can be accomplished through the timed task scheduling logic in the temperature monitoring terminal APP. The temperature data of each sensor is displayed in real time in the form of a chart or list through the user interface UI, allowing the user to quickly identify any abnormal situation.

[0059] Encapsulate the collected temperature data and sensor ID into a structured data format (such as JSON) and upload it to the cloud server through a secure data transmission protocol. You can use HTTPS POST request to upload. Establish a database in the cloud, such as using a cloud database service, to store, retrieve and analyze this data.

[0060] Use the high temperature alarm model to set temperature thresholds according to different application environments. For example, in food processing applications, you can set 90°C as the high temperature threshold. When the temperature reported by the temperature sensor exceeds the preset threshold, the alarm is triggered. Alarm methods include push text notifications, SMS, etc.

[0061] Use the temperature trend prediction model to make predictions: First, clean and format the historical temperature data to remove noise and outliers to improve the accuracy of the prediction model;

[0062] Use machine learning algorithms, such as time series prediction models, to predict temperature trends over a period of time. Use historical temperature data to train these models, and optimize model parameters through cross-validation and other techniques. Integrate the trained models into the temperature monitoring terminal APP, call the prediction model interface in real time, and feed back the prediction results to users to help them make corresponding adjustments and preparations.

[0063] In order to enable the temperature monitoring terminal APP to allow users to operate and control the functions related to the temperature sensor through voice commands, especially in situations where manual operation is inconvenient, this embodiment provides the following technical solutions:

[0064] The received voice signal is converted into text information through the voice recognition model built into the application layer in the temperature monitoring terminal APP.

[0065] Through the voice-to-text function in the natural language processing algorithm, the voice information is converted into text, and keywords are extracted from the text to understand the user's intention. In this embodiment, algorithms such as TF-IDF and deep learning-based embedding methods can be used to identify important words.

[0066] At the same time, context understanding is achieved through semantic analysis techniques in natural language processing, such as semantic parse trees or using pre-trained Transformer models (such as BERT).

[0067] Then, the extracted keywords are matched with the preset control instruction set. Once the match is successful, the temperature monitoring terminal APP will execute the corresponding instructions.

[0068] When the temperature monitoring terminal APP successfully matches a command for temperature query through voice recognition processing, for example, the user's voice information is "query the current temperature status", the system recognizes the keywords "query" and "temperature" through analysis. The "query temperature" command in the control instruction set is matched. After confirming that the command matches the query task, the internal logic of the APP triggers a temperature query request and prepares to obtain data from the connected temperature sensor.

[0069] Send data collection requests to all or specified temperature sensors via a wireless network. Each temperature sensor responds to these requests and sends back their current temperature readings to the temperature monitoring terminal APP. The APP needs to verify the received data to ensure its accuracy and integrity. This includes checking the format correctness of the data and comparing historical data to exclude outliers. The collected temperature data and the corresponding sensor identification IDs need to be organized into a format that is easy to read and understand. For example, the data for each sensor can be structured as a list or dictionary. The temperature monitoring terminal APP sends this structured data to the display layer. The display layer can be a graphical user interface within the temperature monitoring terminal APP, which shows real-time temperature values and the health status of the sensors.

[0070] When the user's voice command is recognized and matched to the "high temperature alarm" command in the command set, the high temperature alarm task is executed. Specifically:

[0071] The temperature monitoring terminal APP first calls the built-in pre-trained high temperature alarm model, which can determine whether there is a high temperature anomaly based on the input temperature data.

[0072] The high temperature alarm model presets a high temperature threshold T0 and a configurable error value M (such as ±1°C) to determine when to trigger the alarm.

[0073] The temperature monitoring terminal APP sends requests to each connected temperature sensor to collect its real-time temperature data. After the data of each sensor is sent back, the temperature monitoring terminal APP adds the collection timestamp to it. The temperature data with the timestamp is sent to the cloud server.

[0074] Then, the high temperature alarm model compares the actually collected temperature value T with the preset threshold T0. Considering the error value M, if |T - T0| < M, it is determined as a high temperature situation. Once a high temperature situation is detected, a warning text message containing the specific temperature value, time, and possible impacts or recommended actions is immediately generated.

[0075] The generated alarm information is first presented on the display layer of the local device so that on-site personnel can take immediate action. The alarm information is also sent to the cloud server, where an information timestamp is added. The cloud server combines the information timestamp with the previously aligned collection timestamp to form an "alarm-temperature feature vector". Multiple "alarm-temperature feature vectors" are aggregated into an alarm feature set Q in the cloud, which provides a basis for subsequent data analysis. For example, these data can be analyzed to optimize the temperature control strategy or improve the sensor layout.

[0076] Users can remotely access these alarm information through the cloud service interface, enabling them to monitor and manage temperature anomalies from any location.

[0077] When the user's voice input is recognized as temperature prediction related instructions, the temperature monitoring terminal APP will call the integrated temperature trend prediction model.

[0078] The temperature monitoring terminal APP collects current and historical temperature data from all connected temperature sensors. This data provides the necessary input for the model to help it learn and predict temperature trends.

[0079] All collected temperature data will be normalized.

[0080] The normalized temperature data is sent as input variables to the temperature trend prediction model to predict future trends. The model will output a predicted value, and based on the prediction results, the system will determine that a high temperature alarm will occur.

[0081] If the prediction results indicate that high temperatures will occur, the temperature monitoring terminal APP will obtain the pre-set high temperature alarm text information from the data layer and convert the high temperature warning text information into voice information using the voice processing model integrated in the APP. This step is to convert the text content into an audio signal that can be played through the speaker through speech synthesis technology.

[0082] The generated voice information is output through an external voice broadcast device to ensure that people on site can clearly hear the high temperature warning and respond in time.

[0083] The construction and training process of the temperature trend prediction model is as follows:

[0084] Constructing a temperature trend prediction model through regression algorithm:

[0085] ,in Indicates the number of temperature sensors, They represent the regression coefficients, is the intercept, To modify the variable, Indicates time Real-time temperature of multiple temperature sensors;

[0086] Obtain the alarm feature set Q from the cloud server;

[0087] Extracting historical temperature feature information and alarm feature information from the alarm feature set;

[0088] The extracted historical temperature feature information is used to form a temperature time series according to the acquisition timestamp. ;

[0089] According to the information time error, the extracted alarm feature information constitutes the alarm time series ;

[0090] right The historical temperature feature information in the temperature feature set is normalized to form a temperature feature set ,right After the alarm feature information in the quantified form, the alarm feature set ;

[0091] The temperature feature set and the alarm feature set are divided into a training set and a test set. The temperature trend prediction model is trained with the training set, and the performance of the temperature trend prediction model is tested with the test set.

[0092] The present invention also provides a multi-point temperature measurement monitoring system, wherein the system is used to execute a multi-point temperature measurement monitoring method.

[0093] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the multi-point temperature measurement and monitoring method.

[0094] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0095] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0096] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A multi-point temperature measurement and monitoring method, characterized in that: The method includes: Setting a plurality of temperature sensors inside the probe, and the plurality of temperature sensors are linearly arranged inside the probe; Establishing a data connection between the temperature sensors and the temperature monitoring terminal APP; The temperature monitoring terminal APP assigns a display button to each temperature sensor and receives the display button operation information; the temperature monitoring terminal APP stores the identification ID of each temperature sensor, and each identification ID corresponds uniquely to the display button; specifically: when a new temperature sensor is added to the system, the temperature monitoring terminal APP records the sensor through an automated registration process. Once the sensor is identified, the system automatically assigns a unique identification ID to it; and in the layout file of the application, a Button control is added for each temperature sensor and a unique ID is assigned to it; Establishing a data connection between the temperature monitoring terminal APP and the cloud server, and the temperature monitoring terminal APP forms a binary tuple with the temperature data and the identification ID of the obtained temperature sensor and sends this binary tuple to the cloud server; The temperature monitoring terminal APP stores a voice processing model and a control instruction set. The control instruction set stores multiple instructions for performing different tasks. The temperature monitoring terminal APP analyzes the received voice signal through the voice processing model and executes corresponding instructions according to the analysis results to complete temperature query tasks, temperature prediction tasks, and high-temperature alarm tasks; If the instruction in the instruction set that matches the keyword is for the high-temperature alarm task, then the following steps are carried out: calling the high-temperature alarm model; collecting the real-time temperature data of multiple temperature sensors, adding a collection timestamp to the temperature data, and then sending the temperature data with the collection timestamp added to the cloud server; performing the high-temperature alarm task through the high-temperature alarm model; specifically: comparing the real-time temperature with the preset high-temperature threshold T0. If |T - T0| < M, then generating a warning text message and sending it to the display layer for display; where T is the real-time temperature, M is the error value, and M > 0; then adding an information timestamp to the warning text message and sending it to the cloud server. After aligning the information timestamp and the collection timestamp in the cloud server, generating an alarm-temperature feature vector, and multiple alarm-temperature feature vectors form an alarm feature set Q. Optimizing the temperature control strategy or improving the sensor layout by analyzing the alarm feature set Q; the temperature sensor is a thermocouple and / or a thermistor, and the number of the temperature sensors is not less than 3; The temperature monitoring terminal APP is divided into three-layer architecture, namely a data layer, an application layer, and a presentation layer; The data layer is used to store and manage the data of the temperature sensors and the preset high-temperature alarm text information; the application layer is used to process business logic, and the business logic includes data analysis and an alarm mechanism; a temperature trend prediction model and a voice processing model are set in the application layer; The presentation layer is used to complete the direct interaction tasks with the user, and the direct interaction tasks include real-time display of temperature, driving an external voice device for voice broadcast, and collecting the operation information of the display button; the operation information includes touch and press operations; The temperature monitoring terminal APP application layer stores a temperature monitoring module, which is used to regularly query and display the temperature information of each temperature sensor, and send the temperature information and the identification ID of the corresponding temperature sensor to the cloud service storage. Users can remotely obtain the temperature sensor information and the corresponding temperature data by connecting to the cloud server. The temperature information can also be used for later data analysis; A high temperature alarm model and a temperature trend prediction model are set in the temperature sensor APP application layer, and the high temperature alarm model and the temperature trend prediction model are constructed through a machine learning algorithm.

2. A multi-point temperature measurement and monitoring method according to claim 1, characterized in that: The temperature monitoring terminal APP stores the ID information of the temperature monitoring terminal itself, the number of temperature sensors, the identification ID of each temperature sensor and the corresponding display button information through the data layer; The ID information of the temperature monitoring terminal itself, the number information of the temperature sensors, the identification ID of each temperature sensor and the corresponding display button information are loaded when the temperature monitoring terminal APP is initialized, so that the temperature sensor can work normally according to the preset configuration.

3. A multi-point temperature measurement and monitoring method according to claim 2, characterized in that: When the user operates the corresponding display button, the temperature monitoring terminal APP sends a command to query the temperature data of the corresponding temperature sensor.

4. A multi-point temperature measurement and monitoring method according to claim 1, characterized in that: Each temperature sensor is assigned a unique identification ID through RFID technology. The identification ID is stored in the cloud server and bound to the display button in the temperature monitoring terminal APP.

5. A multi-point temperature measurement and monitoring method according to claim 2, characterized in that: The speech processing model is set in the application layer. The speech processing model recognizes and processes the speech signal through a natural language processing algorithm, converts the input speech signal into text information, extracts keywords from the text information using a keyword extraction algorithm, and matches the keywords with instructions in a control instruction set using a context understanding algorithm. If the match is successful, the corresponding instruction is executed.

6. A multi-point temperature measurement and monitoring method according to claim 1, characterized in that: The corresponding instructions are executed according to the analysis results to complete the temperature query task, temperature prediction task and high temperature alarm task, including the following steps:

1. If the instruction matching the keyword in the instruction set is for a temperature query task, perform the following steps: Collect real-time temperature data from multiple temperature sensors; The temperature information and the identification ID of the corresponding temperature sensor are sent to the display layer for display; Users remotely obtain alarm information through cloud services; 2. If the instruction in the instruction set that matches the keyword is for the temperature prediction task, perform the following steps: Call the trained temperature trend prediction model; Collect temperature data from multiple temperature sensors, normalize the temperature data, and use the data as input variables of the temperature trend prediction model and input them into the temperature trend prediction model; Output the alarm characteristic value at a future moment through the temperature trend prediction model ; when When , it is predicted that a high temperature alarm will occur at a certain moment in the future, and the preset high temperature alarm text information is obtained from the data layer; The high temperature warning text information is sent to the voice processing model, and the voice processing model generates voice information from the high temperature warning text information and sends it to the presentation layer, and the voice output of the high temperature warning information is performed through an external voice broadcasting device.

7. A multi-point temperature measurement and monitoring system, characterized in that: The system is used to execute a multi-point temperature measurement and monitoring method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a multi-point temperature measurement and monitoring method as described in any one of claims 1 to 6.

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