Method and system for realizing talkback function of intelligent wearable device

Abstract

The invention discloses a method and a system for realizing a talkback function of intelligent wearable equipment, and relates to the field of talkback of the intelligent wearable equipment, and the method comprises the steps: setting an access verification mechanism for a user; talkback requirements and environment parameters of the intelligent wearable device are collected in real time; determining a talkback transmission mode, and carrying out talkback automatic transmission by using a talkback transmission channel; establishing a data storage, processing and analysis platform; performing relation mining on historical data stored in the database by adopting an association rule algorithm; displaying data generated in equipment operation to an operator in a visual chart manner through a big data visual page; and learning the operation mode of the equipment by using a long-short term memory network algorithm. And the information acquisition module and the information processing module are arranged to obtain related information to predict faults, so that the working efficiency of talkback is ensured to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the field of intercom for smart wearable devices, and in particular to a method and system for realizing an intercom function for smart wearable devices. Background Art

[0002] Wearable smart devices are a general term for wearable devices that are designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothing and shoes. In a broad sense, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring. There are many intercom functions for smart wearable devices.

[0003] Existing smart wearable devices are prone to audio data transmission distortion and other deficiencies in intercom functions due to environmental interference during the intercom process, resulting in reduced intercom work efficiency. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for realizing the intercom function of a smart wearable device are provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for realizing an intercom function of a smart wearable device, comprising: An access verification mechanism is set up for users, and the corresponding intercom function permissions are opened according to the user's verification results. The intercom function permissions include real-time voice communication, multi-channel switching, GPS positioning, emergency alarm function, SMS push and WeChat push; Utilize sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, and wake up the intercom function according to the intercom requirements; Determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automation transmission; Obtain the influence relationship between the intercom function of smart wearable devices and environmental variables, and use association rule algorithms to mine the relationship of historical data stored in the database; Analyze the historical data accumulated during the operation of smart wearable devices, and display the data generated during the operation of smart wearable devices to operators in the form of visual charts through the big data visualization page; The long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of smart wearable devices and make corrections in advance.

[0006] Preferably, the use of sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time specifically includes: Monitor the intercom demand voice data of the smart wearable device in real time, extract features of the intercom demand voice data, and obtain at least one demand feature; Aggregate at least one demand feature as an intercom demand; Get the intercom path of the smart wearable device and use the area where the intercom path passes as the environmental area; Intercom interference factors are acquired in the environmental area and summarized as environmental parameters.

[0007] Preferably, the determining of the intercom transmission mode and using the intercom transmission channel to perform intercom automation transmission specifically includes: Smart wearable devices use wireless ZigBee technology for intercom; Assign unique IP addresses and port numbers to the smart wearable device and the target device for the smart wearable device to intercom, and accurately transmit data to the target device; Acquiring parameter settings of the wireless ZigBee technology to obtain at least one transmission parameter; Evenly divide the value range of the transmission parameter to obtain at least one parameter point; Randomly combining parameter points of different transmission parameters to obtain at least one parameter point combination; Use the configuration of parameter point combination to perform intercom transmission of smart wearable devices and obtain the reduction ratio of environmental parameters on the accuracy of intercom function. The parameter point combination with the smallest weakening ratio is used as the target parameter, and the wireless ZigBee technology using the target parameter is used as the intercom transmission method.

[0008] Preferably, the obtaining of the influence relationship between the intercom function of the smart wearable device and the environmental variables and the use of an association rule algorithm to perform relationship mining on historical data stored in a database specifically includes: Scan the historical data stored in the database, count the number of times each data appears, and form a candidate data set. The historical data consists of the values ​​of environmental variables and the impact of environmental variables on intercom requirements. Data can appear repeatedly in the historical data. Determine whether the number of occurrences of each data in the candidate data set is greater than the preset frequent threshold. If so, output the data as a frequent data set; if not, do not output it; Randomly combine the data in the frequent data set and update the combined data to the candidate data set; Scan the historical data stored in the database and count the combined data that appear simultaneously in the candidate data set; Determine whether the number of occurrences of each group of data in the candidate data set is greater than the preset frequent threshold. If so, update the group of data to the frequent data set and mark the group of data as the associated data set. If not, do not output it. The associated data groups in the frequent data set are fitted to obtain the impact prediction function, in which the value of the environmental variable is the independent variable and the impact value of the environmental variable on the intercom demand is the dependent variable.

[0009] Preferably, the data analysis of the historical data accumulated during the operation of the smart wearable device and displaying the data generated during the operation of the smart wearable device to the operator in the form of a visual chart through a big data visualization page specifically includes: The smart wearable device is referred to as a device to obtain an overview of the device information. The operator can view the basic information of the device through charts; Statistics of equipment inspection data, displaying historical inspection information of equipment through charts, and using red line charts to display the comparison trend of inspection results over the same period; Display device status data, and use pie charts to show the probability of normal operation of the device and intercom abnormality this month; Statistics of equipment maintenance data, showing in a table the relevant information for maintenance when equipment abnormalities occur, including equipment information, maintenance reasons, maintenance measures, maintenance personnel contact information and maintenance time; The device operation frequency is counted and displayed in the form of a bar chart.

[0010] Preferably, the use of the long short-term memory network algorithm to predict the implementation deviation of the intercom function of the smart wearable device and correct it in advance specifically includes: According to the gated recurrent unit formula, the input gate, forget gate and output gate functions are calculated respectively; Construct a long short-term memory neural network model based on input gate, forget gate and output gate functions; The device-related data is input into the long short-term memory neural network model, and the results of the input gate, forget gate and output gate functions are averaged to obtain the predicted value of the device data at each time. The device-related data is the actual fluctuation value of the environmental variables when the smart wearable device is actually talking. Obtaining a value range of the actual fluctuation value, evenly dividing the value range of the actual fluctuation value, and obtaining at least one local interval of the independent variable; Integrate the impact prediction function over the local interval of the independent variable and divide it by the length of the local interval of the independent variable to obtain the characteristic value; Use the minimum and maximum values ​​of the characteristic values ​​to form a normal operating range; Determine whether the predicted value exceeds the normal operating range. If so, correct the time corresponding to the predicted value. If not, do not correct it. During correction, the parameters of the wireless ZigBee technology are adjusted to the correction parameters, and the ratio of the correction parameters to the target parameters is equal to the ratio of the midpoint of the normal operating range to the predicted value; The gated recurrent unit formula is: , In the formula, , , They are the input gate, forget gate and output gate functions respectively. , , are the parameter matrices of the input gate, forget gate, and output gate from time t-1 to time t, , , are the parameter matrices from the input layer to the current layer of the input gate, forget gate, and output gate, respectively. , They are the equipment-related data at time t-1 and time t respectively.

[0011] Furthermore, a system for realizing the intercom function of a smart wearable device is proposed, which is used to realize the method for realizing the intercom function of the smart wearable device as described above, including: An information collection module is used to set an access verification mechanism for users, and open corresponding intercom function permissions according to the user's verification results, use sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automatic transmission; An information processing module is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, use the association rule algorithm to mine the relationship of the historical data stored in the database, and perform data analysis on the historical data accumulated during the operation of the smart wearable device. The data generated during the operation of the smart wearable device is displayed to the operator in the form of a visual chart through a big data visualization page, and the long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of the smart wearable device and make corrections in advance.

[0012] Optionally, the information collection module specifically includes: A user verification unit, which is used to set an access verification mechanism for the user and open corresponding intercom function permissions according to the user's verification result; An intercom wake-up unit, which is used to collect intercom requirements and environmental parameters of the smart wearable device in real time using sensors and smart terminals, and wake up the intercom function according to the intercom requirements; An intercom transmission unit is used to determine an intercom transmission mode and use an intercom transmission channel to perform intercom automation transmission.

[0013] Optionally, the information processing module specifically includes: A relationship mining unit, wherein the relationship mining unit is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, and uses an association rule algorithm to perform relationship mining on the historical data stored in the database; A data visualization unit, which is used to analyze historical data accumulated during the operation of the smart wearable device, and to display the data generated during the operation of the smart wearable device to the operator in the form of a visual chart through a big data visualization page; The deviation prediction unit is used to predict the implementation deviation of the intercom function of the smart wearable device by using the long short-term memory network algorithm and make corrections in advance.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By setting up information collection modules and information processing modules to obtain relevant information to predict faults, the risk of deviation in intercom effects can be minimized. Automated operation and maintenance is a process-oriented framework that adds various possible situations to the process, sets intercom parameters for each environmental parameter, monitors the system at all times, and predicts deviations. Once the intercom function deviates, it will be corrected in proportion to ensure the working efficiency of the intercom. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for implementing the intercom function of a smart wearable device of the present invention; Figure 2 A flow chart of a method for real-time acquisition of intercom requirements and environmental parameters of a smart wearable device according to the present invention; Figure 3 A flow chart of the method for determining an intercom transmission mode and using an intercom transmission channel to perform intercom automation transmission according to the present invention; Figure 4 A flow chart of a method for information mining of historical data stored in a database according to the present invention; Figure 5 This is a flow chart of a method for analyzing historical data accumulated during operation of a smart wearable device according to the present invention; Figure 6 This is a flow chart of the long short-term memory network algorithm method of the present invention. DETAILED DESCRIPTION

[0016] 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 may think of other obvious variations.

[0017] Reference Figure 1 As shown, a method for realizing the intercom function of a smart wearable device includes: An access verification mechanism is set up for users, and the corresponding intercom function permissions are opened according to the user's verification results. The intercom function permissions include real-time voice communication, multi-channel switching, GPS positioning, emergency alarm function, SMS push and WeChat push; Utilize sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, and wake up the intercom function according to the intercom requirements; Determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automation transmission; Obtain the influence relationship between the intercom function of smart wearable devices and environmental variables, and use association rule algorithms to mine the relationship of historical data stored in the database; Analyze the historical data accumulated during the operation of smart wearable devices, and display the data generated during the operation of smart wearable devices to operators in the form of visual charts through the big data visualization page; The long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of smart wearable devices and make corrections in advance.

[0018] Reference Figure 2 As shown in the figure, using sensors and smart terminals, real-time collection of intercom requirements and environmental parameters of smart wearable devices includes: Monitor the intercom demand voice data of the smart wearable device in real time, extract features of the intercom demand voice data, and obtain at least one demand feature; Aggregate at least one demand feature as an intercom demand; Get the intercom path of the smart wearable device and use the area where the intercom path passes as the environmental area; Intercom interference factors are acquired in the environmental area and summarized as environmental parameters.

[0019] Sensors are the basic components of the Internet of Things. They can sense and collect various data from smart wearable devices in real time. Smart terminals are the data processing and transmission centers in the Internet of Things. They can receive data from sensors and perform preliminary processing and storage.

[0020] Reference Figure 3 As shown, determining the intercom transmission mode, using the intercom transmission channel, and performing intercom automation transmission specifically include: Smart wearable devices use wireless ZigBee technology for intercom; Assign unique IP addresses and port numbers to the smart wearable device and the target device for the smart wearable device to intercom, and accurately transmit data to the target device; Acquiring parameter settings of the wireless ZigBee technology to obtain at least one transmission parameter; Evenly divide the value range of the transmission parameter to obtain at least one parameter point; Randomly combining parameter points of different transmission parameters to obtain at least one parameter point combination; Use the configuration of parameter point combination to perform intercom transmission of smart wearable devices and obtain the reduction ratio of environmental parameters on the accuracy of intercom function. The parameter point combination with the smallest weakening ratio is used as the target parameter, and the wireless ZigBee technology using the target parameter is used as the intercom transmission method.

[0021] In the implementation method of the intercom function of the smart wearable device, for each environmental parameter, the transmission parameters need to be reasonably set to ensure the effect of the intercom function. In this solution, the wireless ZigBee technology is used, so it is necessary to reasonably adjust its parameters.

[0022] Reference Figure 4 As shown in the figure, the influence relationship between the intercom function of the smart wearable device and the environmental variables is obtained, and the association rule algorithm is used to perform relationship mining on the historical data stored in the database, which specifically includes: Scan the historical data stored in the database, count the number of times each data appears, and form a candidate data set. The historical data consists of the values ​​of environmental variables and the impact of environmental variables on intercom requirements. Data can appear repeatedly in the historical data. Determine whether the number of occurrences of each data in the candidate data set is greater than the preset frequent threshold. If so, output the data as a frequent data set; if not, do not output it; Randomly combine the data in the frequent data set and update the combined data to the candidate data set; Scan the historical data stored in the database and count the combined data that appear simultaneously in the candidate data set; Determine whether the number of occurrences of each group of data in the candidate data set is greater than the preset frequent threshold. If so, update the group of data to the frequent data set and mark the group of data as the associated data set. If not, do not output it. The associated data groups in the frequent data set are fitted to obtain the impact prediction function, in which the value of the environmental variable is the independent variable and the impact value of the environmental variable on the intercom demand is the dependent variable.

[0023] The association rule algorithm is a very classic task in massive data mining. Its main goal is to try to mine frequent items and corresponding association rules from a series of transaction sets. In actual situations, the values ​​of environmental variables and the impact values ​​of environmental variables on intercom needs may be scattered or corresponding. For corresponding situations, there is no need to use the above method to perform the impact prediction function, but for scattered situations, the above method needs to be used to perform the impact prediction function.

[0024] Reference Figure 5 As shown, the historical data accumulated during the operation of the smart wearable device is analyzed, and the data generated during the operation of the smart wearable device is displayed to the operator in the form of visual charts through the big data visualization page, including: The smart wearable device is referred to as a device to obtain an overview of the device information. The operator can view the basic information of the device through charts; Statistics of equipment inspection data, displaying historical inspection information of equipment through charts, and using red line charts to display the comparison trend of inspection results over the same period; Display device status data, and use pie charts to show the normal operation of the device and the probability of intercom abnormality this month; Statistics of equipment maintenance data, showing in a table the relevant information for maintenance when equipment abnormalities occur, including equipment information, maintenance reasons, maintenance measures, maintenance personnel contact information and maintenance time; The device operation frequency is counted and displayed in the form of a bar chart.

[0025] The function of this part is to analyze the historical data generated by the operation of smart wearable devices, provide operators with clear equipment operation status, and assist operators in making management decisions.

[0026] Reference Figure 6 As shown in the figure, the long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of the smart wearable device and make corrections in advance, including: According to the gated recurrent unit formula, the input gate, forget gate and output gate functions are calculated respectively; Construct a long short-term memory neural network model based on input gate, forget gate and output gate functions; The device-related data is input into the long short-term memory neural network model, and the results of the input gate, forget gate and output gate functions are averaged to obtain the predicted value of the device data at each time. The device-related data is the actual fluctuation value of the environmental variables when the smart wearable device is actually talking. Obtaining a value range of the actual fluctuation value, evenly dividing the value range of the actual fluctuation value, and obtaining at least one local interval of the independent variable; Integrate the impact prediction function over the local interval of the independent variable and divide it by the length of the local interval of the independent variable to obtain the characteristic value; Use the minimum and maximum values ​​of the characteristic values ​​to form a normal operating range; Determine whether the predicted value exceeds the normal operating range. If so, correct the time corresponding to the predicted value. If not, do not correct it. During correction, the parameters of the wireless ZigBee technology are adjusted to the correction parameters, and the ratio of the correction parameters to the target parameters is equal to the ratio of the midpoint of the normal operating range to the predicted value; The gated recurrent unit formula is: , In the formula, , , They are the input gate, forget gate and output gate functions respectively. , , are the parameter matrices of the input gate, forget gate, and output gate from time t-1 to time t, , , are the parameter matrices from the input layer to the current layer of the input gate, forget gate, and output gate, respectively. , They are the equipment-related data at time t-1 and time t respectively.

[0027] Recurrent neural networks are often used in time series related tasks. Common variants include long short-term memory network algorithm and gated recurrent unit. The traditional neural network structure is feedforward, and there is no connection between nodes in each layer. However, the parameters of each layer of the recurrent neural network are shared. The output of the current layer not only considers the output of the previous layer, but also the output of the hidden layer at the previous moment. Here, the long short-term memory neural network model is mainly used to predict the fluctuation of the intercom function. Because the environmental parameters are not constant, when the predicted fluctuation is within the permitted conditions, no correction is made. However, when the fluctuation is too large, correction is required in advance to avoid compromising the effect of the intercom function.

[0028] Further, based on the same inventive concept as the above-mentioned method for realizing the intercom function of the smart wearable device, the present solution also proposes a system for realizing the intercom function of the smart wearable device, including: An information collection module is used to set an access verification mechanism for users, and open corresponding intercom function permissions according to the user's verification results, use sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automatic transmission; An information processing module is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, use the association rule algorithm to mine the relationship of the historical data stored in the database, and perform data analysis on the historical data accumulated during the operation of the smart wearable device. The data generated during the operation of the smart wearable device is displayed to the operator in the form of a visual chart through a big data visualization page, and the long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of the smart wearable device and make corrections in advance.

[0029] The information collection modules include: A user verification unit, which is used to set an access verification mechanism for the user and open corresponding intercom function permissions according to the user's verification result; An intercom wake-up unit, which is used to collect intercom requirements and environmental parameters of the smart wearable device in real time using sensors and smart terminals, and wake up the intercom function according to the intercom requirements; An intercom transmission unit is used to determine an intercom transmission mode and use an intercom transmission channel to perform intercom automation transmission.

[0030] The information processing module specifically includes: A relationship mining unit, wherein the relationship mining unit is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, and uses an association rule algorithm to perform relationship mining on the historical data stored in the database; A data visualization unit, which is used to analyze historical data accumulated during the operation of the smart wearable device, and to display the data generated during the operation of the smart wearable device to the operator in the form of a visual chart through a big data visualization page; The deviation prediction unit is used to predict the implementation deviation of the intercom function of the smart wearable device by using the long short-term memory network algorithm and make corrections in advance.

[0031] Furthermore, the present solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, the method for implementing the above-mentioned intercom function of the smart wearable device is executed.

[0032] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0033] To sum up, the advantages of the present invention are: by setting an information collection module and an information processing module to obtain relevant information to predict faults, the risk of deviation in intercom effects is minimized; automated operation and maintenance is a process-oriented framework, various possible situations are added to the process, and intercom parameters are set for each environmental parameter, the system is monitored at all times, and deviations are predicted; once the intercom function deviates, a corresponding proportion of correction is made to ensure the working efficiency of the intercom.

[0034] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for realizing the intercom function of a smart wearable device, characterized in that: include: An access verification mechanism is set up for users, and the corresponding intercom function permissions are opened according to the user's verification results. The intercom function permissions include real-time voice communication, multi-channel switching, GPS positioning, emergency alarm function, SMS push and WeChat push; Utilize sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, and wake up the intercom function according to the intercom requirements; Determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automation transmission; Obtain the influence relationship between the intercom function of smart wearable devices and environmental variables, and use association rule algorithms to mine the relationship of historical data stored in the database; Analyze the historical data accumulated during the operation of smart wearable devices, and display the data generated during the operation of smart wearable devices to operators in the form of visual charts through the big data visualization page; The long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of smart wearable devices and make corrections in advance.

2. The method for realizing the intercom function of a smart wearable device according to claim 1, characterized in that: The use of sensors and smart terminals to collect real-time intercom requirements and environmental parameters of smart wearable devices specifically includes: Monitor the intercom demand voice data of the smart wearable device in real time, extract features of the intercom demand voice data, and obtain at least one demand feature; Aggregate at least one demand feature as an intercom demand; Get the intercom path of the smart wearable device and use the area where the intercom path passes as the environmental area; Intercom interference factors are acquired in the environmental area and summarized as environmental parameters.

3. The method for realizing the intercom function of a smart wearable device according to claim 2, characterized in that: Determining the intercom transmission mode and using the intercom transmission channel to perform intercom automation transmission specifically includes: Smart wearable devices use wireless ZigBee technology for intercom; Assign unique IP addresses and port numbers to the smart wearable device and the target device for the smart wearable device to intercom, and accurately transmit data to the target device; Acquiring parameter settings of the wireless ZigBee technology to obtain at least one transmission parameter; Evenly divide the value range of the transmission parameter to obtain at least one parameter point; Randomly combining parameter points of different transmission parameters to obtain at least one parameter point combination; Use the configuration of parameter point combination to perform intercom transmission of smart wearable devices and obtain the reduction ratio of environmental parameters on the accuracy of intercom function. The parameter point combination with the smallest weakening ratio is used as the target parameter, and the wireless ZigBee technology using the target parameter is used as the intercom transmission method.

4. The method for realizing the intercom function of a smart wearable device according to claim 3, characterized in that: The acquisition of the influence relationship between the intercom function of the smart wearable device and the environmental variables and the use of an association rule algorithm to perform relationship mining on the historical data stored in the database specifically include: Scan the historical data stored in the database, count the number of times each data appears, and form a candidate data set. The historical data consists of the values ​​of environmental variables and the impact of environmental variables on intercom requirements. Data can appear repeatedly in the historical data. Determine whether the number of occurrences of each data in the candidate data set is greater than the preset frequent threshold. If so, output the data as a frequent data set; if not, do not output it; Randomly combine the data in the frequent data set and update the combined data to the candidate data set; Scan the historical data stored in the database and count the combined data that appear simultaneously in the candidate data set; Determine whether the number of occurrences of each group of data in the candidate data set is greater than the preset frequent threshold. If so, update the group of data to the frequent data set and mark the group of data as the associated data set. If not, do not output it. The associated data groups in the frequent data set are fitted to obtain the impact prediction function, in which the value of the environmental variable is the independent variable and the impact value of the environmental variable on the intercom demand is the dependent variable.

5. The method for realizing the intercom function of a smart wearable device according to claim 4, characterized in that: The data analysis of the historical data accumulated during the operation of the smart wearable device and displaying the data generated during the operation of the smart wearable device to the operator in the form of a visual chart through a big data visualization page specifically includes: The smart wearable device is referred to as a device to obtain an overview of the device information. The operator can view the basic information of the device through charts; Statistics of equipment inspection data, displaying historical inspection information of equipment through charts, and using red line charts to display the comparison trend of inspection results over the same period; Display device status data, and use pie charts to show the normal operation of the device and the probability of intercom abnormality this month; Statistics of equipment maintenance data, showing in a table the relevant information for maintenance when equipment abnormalities occur, including equipment information, maintenance reasons, maintenance measures, maintenance personnel contact information and maintenance time; The device operation frequency is counted and displayed in the form of a bar chart.

6. The method for realizing the intercom function of a smart wearable device according to claim 5, characterized in that: The method of using the long short-term memory network algorithm to predict the implementation deviation of the intercom function of the smart wearable device and correct it in advance specifically includes: According to the gated recurrent unit formula, the input gate, forget gate and output gate functions are calculated respectively; Construct a long short-term memory neural network model based on input gate, forget gate and output gate functions; The device-related data is input into the long short-term memory neural network model, and the results of the input gate, forget gate and output gate functions are averaged to obtain the predicted value of the device data at each time. The device-related data is the actual fluctuation value of the environmental variables when the smart wearable device is actually talking. Obtaining a value range of the actual fluctuation value, evenly dividing the value range of the actual fluctuation value, and obtaining at least one local interval of the independent variable; Integrate the impact prediction function over the local interval of the independent variable and divide it by the length of the local interval of the independent variable to obtain the characteristic value; Use the minimum and maximum values ​​of the characteristic values ​​to form a normal operating range; Determine whether the predicted value exceeds the normal operating range. If so, correct the time corresponding to the predicted value. If not, do not correct it. During correction, the parameters of the wireless ZigBee technology are adjusted to the correction parameters, and the ratio of the correction parameters to the target parameters is equal to the ratio of the midpoint of the normal operating range to the predicted value; The gated recurrent unit formula is: , In the formula, , , They are the input gate, forget gate and output gate functions respectively. , , are the parameter matrices of the input gate, forget gate, and output gate from time t-1 to time t, , are the parameter matrices from the input layer to the current layer of the input gate, forget gate, and output gate, respectively. , They are the equipment-related data at time t-1 and time t respectively.

7. A system for realizing an intercom function of a smart wearable device, used to realize the method for realizing an intercom function of a smart wearable device according to any one of claims 1 to 6, characterized in that: include: An information collection module is used to set an access verification mechanism for users, and open corresponding intercom function permissions according to the user's verification results, use sensors and smart terminals to collect the intercom requirements and environmental parameters of smart wearable devices in real time, determine the intercom transmission mode, use the intercom transmission channel, and perform intercom automatic transmission; An information processing module is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, use the association rule algorithm to mine the relationship of the historical data stored in the database, and perform data analysis on the historical data accumulated during the operation of the smart wearable device. The data generated during the operation of the smart wearable device is displayed to the operator in the form of a visual chart through a big data visualization page, and the long short-term memory network algorithm is used to predict the implementation deviation of the intercom function of the smart wearable device and make corrections in advance.

8. A system for realizing the intercom function of a smart wearable device according to claim 7, characterized in that: The information collection module specifically includes: A user verification unit, which is used to set an access verification mechanism for the user and open corresponding intercom function permissions according to the user's verification result; An intercom wake-up unit, which is used to collect intercom requirements and environmental parameters of the smart wearable device in real time using sensors and smart terminals, and wake up the intercom function according to the intercom requirements; An intercom transmission unit is used to determine an intercom transmission mode and use an intercom transmission channel to perform intercom automation transmission.

9. A system for realizing the intercom function of a smart wearable device according to claim 8, characterized in that: The information processing module specifically includes: A relationship mining unit, wherein the relationship mining unit is used to obtain the influence relationship between the intercom function of the smart wearable device and the environmental variables, and use an association rule algorithm to perform relationship mining on the historical data stored in the database; A data visualization unit, which is used to analyze historical data accumulated during the operation of the smart wearable device, and to display the data generated during the operation of the smart wearable device to the operator in the form of a visual chart through a big data visualization page; The deviation prediction unit is used to predict the implementation deviation of the intercom function of the smart wearable device by using the long short-term memory network algorithm and make corrections in advance.

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