A method and device for displaying information expansion of monitoring equipment

Abstract

The present application relates to a method and device for presenting information expansion for monitoring equipment, which belongs to the field of equipment information presentation, and the method includes: determining a target data model and an initial presentation mode according to a request instruction, the request instruction includes a user role, the user role includes a tester and a manager, the tester and the manager correspond to different presentation modes respectively, and the target data model includes multiple types of environmental data; when the user role is a tester, the target data model is input into a neural network model to obtain a final presentation mode, and the final presentation mode is a presentation mode in the initial presentation mode; when the user role is a manager, the final presentation mode is obtained according to multiple types of environmental data in the target data model and the warning value corresponding to each type of environmental data; the target data model is presented using the final presentation mode to obtain a presentation result. The present application has the effect of improving the utilization rate of environmental data.

Description

Technical Field

[0001] The present application relates to the field of device information presentation, and in particular to a method and apparatus for presenting extended information of a monitoring device. Background Art

[0002] Monitoring equipment is deployed in the field and is mainly used to collect environmental data on site. For example, monitoring equipment can be a sensor network deployed in a water body or reservoir. The sensor network is used to monitor water conditions in real time, such as water level, water temperature, water quality, etc. It can also be used to monitor equipment conditions, equipment operating status, displacement, soil moisture content and other data in the environment.

[0003] Since the environment where the monitoring equipment is located is usually harsh, such as mudslide areas or reservoirs, the hardware configuration of the monitoring equipment is relatively simple. This can reduce the load on the monitoring equipment and prevent multiple hardware from interfering with each other. Therefore, the monitoring equipment is usually not equipped with a screen, but sends the collected environmental data to the cloud, which is processed and stored by the cloud. When the user needs to use the environmental data, the environmental data can be retrieved from the cloud.

[0004] However, the current cloud does not consider the purpose of the user's environmental data and the user's usage rights, so when returning environmental data, it is sent to the user in a fixed presentation mode, resulting in the user needing to perform secondary processing on the environmental data according to different purposes after obtaining the environmental data in order to obtain the environmental data they really need. In other words, the current cloud lacks different display modes for different user terminals, so the utilization rate of environmental data is not high. Summary of the invention

[0005] The present application provides a method and device for presenting information expansion for monitoring equipment, which has the characteristic of improving the utilization rate of environmental data.

[0006] The first object of the present application is to provide a method for presenting information expansion for monitoring equipment.

[0007] The above-mentioned application objective 1 of the present application is achieved through the following technical solutions:

[0008] A method for presenting information expansion for monitoring equipment, comprising:

[0009] Determine a target data model and an initial presentation mode according to a request instruction, wherein the request instruction includes a user role, the user role includes a tester and a manager, the tester and the manager correspond to different presentation modes respectively, and the target data model includes multiple types of environmental data;

[0010] When the user role is a tester, the target data model is input into a neural network model to obtain a final presentation mode, wherein the final presentation mode is one of the initial presentation modes;

[0011] When the user role is a manager, a final presentation mode is obtained according to multiple types of environmental data in the target data model and the warning values ​​corresponding to each type of environmental data;

[0012] The target data model is presented using the final presentation mode to obtain a presentation result.

[0013] By adopting the above technical solution, for the obtained request instruction, the target data model and the initial presentation mode are first determined according to the request instruction, and then based on the initial presentation mode, the final presentation mode is further determined, and finally the target data model is presented with the final presentation mode. It can be seen that the present application can realize the use of different target presentation modes to present the target data model according to different request instructions, so that the target data model presented to the testers and managers is more intuitive, thereby reducing the probability of the testers and managers performing secondary processing on the obtained target data model, thereby achieving the purpose of improving the utilization rate of the environmental data obtained by the testers and managers.

[0014] In a preferred example, the present application may be further configured as follows: determining the initial presentation mode according to the request instruction includes:

[0015] A presentation mode corresponding to the user role is determined, and the presentation mode corresponding to the user role is marked as an initial presentation mode.

[0016] By adopting the above technical solution, the present application is configured with different presentation modes according to different user roles, so as to facilitate the subsequent targeted presentation of the required environmental data to the user.

[0017] In a preferred example, the present application may be further configured as follows: the initial presentation mode includes a line chart, a bar chart, and a scatter chart;

[0018] When the user role is a tester, the target data model is input into a neural network model to obtain a final presentation mode, including:

[0019] Calculating the association degree of multiple types of environmental data included in the target data model;

[0020] Obtaining a classification level according to the association degree, wherein one classification level corresponds to one initial presentation mode;

[0021] An initial presentation mode corresponding to the classification level is determined as a final presentation mode.

[0022] By adopting the above technical solution, for testers, since they are performing maintenance or testing on site, they only need to obtain environmental data related to the equipment they are responsible for maintenance. Other irrelevant data will interfere with the maintenance personnel's maintenance of the equipment they are responsible for. In other words, testers are more sensitive to data than managers, so this application gives priority to matching testers with initial presentation modes such as line graphs, bar graphs, and scatter graphs. Furthermore, based on the correlation between the environmental data included in the target data model, a presentation mode that can better present the environmental data included in the target data model is determined, providing technical support for reducing users' secondary processing of the acquired environmental data.

[0023] In a preferred example, the present application may be further configured as follows: the calculation of the association degree of the multiple types of environmental data included in the target data model includes:

[0024] Obtaining the degree of association according to the number of environmental data having an association relationship among all environmental data included in the target data model; and / or

[0025] The degree of association is obtained according to the similarity scores of the environmental data with association relationship among all the environmental data included in the target data model, wherein the association relationship means that the similarity between two of the environmental data or between a plurality of the environmental data is not equal to zero.

[0026] By adopting the above technical solutions, the present application provides multiple methods for calculating the association of multiple types of environmental data included in the target data model. In actual use, you can choose according to your needs, so that the applicability of the present application is higher.

[0027] In a preferred example, the present application may be further configured as follows: the initial presentation mode includes a three-dimensional model format and an image format;

[0028] When the user role is a manager, the final presentation mode is obtained according to the multiple types of environmental data in the target data model and the warning values ​​corresponding to each type of environmental data, including:

[0029] Determine whether any type of environmental data among multiple types of environmental data exceeds a corresponding warning value or warning range;

[0030] If so, the image format is determined as the final presentation mode;

[0031] If not, the three-dimensional model format is determined as the final presentation format.

[0032] By adopting the above technical solution, for managers, since managers usually monitor the site remotely, they need to control the overall situation, and global control requires support from various types of environmental data. In this application, it can also be considered that managers are more sensitive to images than testers, so presentation modes such as three-dimensional model formats and image formats are matched for managers. In actual application, a presentation mode that can better present the environmental data in the target data model will be determined based on whether the environmental data included in the target data model exceeds its corresponding warning value or warning range, providing technical support for reducing users' secondary processing of the acquired environmental data.

[0033] In a preferred example, the present application may be further configured as follows: after presenting the target data model in the final presentation mode to obtain a presentation result, the method further includes:

[0034] Determining the mutated environmental data according to the environmental data included in the target data model;

[0035] Display environmental data for the mutation.

[0036] By adopting the above technical solution, the present application also displays mutated environmental data, thereby facilitating management personnel to quickly pay attention to the mutated environmental data and take timely precautions to fully realize the value of the presented environmental data.

[0037] In a preferred example, the present application may be further configured as follows: the request instruction includes a geographic location and a user number, and the user number corresponds to the query authority;

[0038] Determining the target data model according to the request instruction includes:

[0039] determining an initial data model according to the geographic location;

[0040] The target data model is determined according to the initial data model and the query authority corresponding to the user number.

[0041] By adopting the above technical solution, the present application first matches an initial data model that covers a wider range of environmental data, and then further matches the target data model based on the initial data model, and finally only presents the target data model to the user, so that the environmental data included in the target data model obtained by the user is the environmental data he needs, further reducing the probability of the user performing secondary processing on the acquired environmental data.

[0042] In a preferred example, the present application may be further configured as follows: before determining the target data model and the initial presentation mode according to the request instruction, the method further includes:

[0043] The request instruction includes the request time and device code;

[0044] Determining the authenticity of the user terminal based on the request time and the device code;

[0045] When the user terminal is determined to be authentic, determining a target data model and an initial presentation mode according to the request instruction;

[0046] When the user terminal is determined not to have authenticity, the user terminal is prohibited from accessing.

[0047] By adopting the above technical solution, the authenticity of the user terminal is determined according to the request time and the device code. When the user terminal is authentic, the environment data required by the user is presented to the user in a targeted manner to prevent the user terminal from maliciously querying the environment data.

[0048] The second purpose of this application is to provide an information expansion presentation device for monitoring equipment.

[0049] The second application objective of the present application is achieved through the following technical solutions:

[0050] An information expansion presentation device for monitoring equipment, comprising:

[0051] A data determination module, configured to determine a target data model and an initial presentation mode according to a request instruction, wherein the request instruction includes a user role, the user role includes a tester and a manager, the tester and the manager correspond to different presentation modes respectively, and the target data model includes multiple types of environmental data;

[0052] The data processing module is used to input the target data model into the neural network model to obtain a final presentation mode when the user role is a tester, and the final presentation mode is one of the initial presentation modes; and is also used to obtain a final presentation mode according to multiple types of environmental data in the target data model and warning values ​​corresponding to each type of environmental data when the user role is a manager:

[0053] The data generation module is used to present the target data model in a final presentation mode to obtain a presentation result.

[0054] In summary, the present application includes at least one of the following beneficial technical effects:

[0055] 1. For testers, since they are performing maintenance or testing on site, they only need to obtain environmental data related to the equipment they are responsible for maintenance. Other irrelevant data will interfere with the maintenance personnel's maintenance of the equipment they are responsible for. In other words, testers are more sensitive to data than managers, so this application gives priority to matching testers with initial presentation modes such as line graphs, bar graphs, and scatter graphs. Furthermore, based on the correlation between the environmental data included in the target data model, a presentation mode that can better present the environmental data included in the target data model is determined, providing technical support for reducing users' secondary processing of the acquired environmental data;

[0056] 2. For managers, since managers usually monitor the site remotely, they need to control the overall situation, and global control requires support from various types of environmental data. In this application, it can also be considered that managers are more sensitive to images than testers, so presentation modes such as three-dimensional model format and image format are matched for managers. In actual application, a presentation mode that can better present the environmental data in the target data model will be determined based on whether the environmental data included in the target data model exceeds its corresponding warning value or warning range, providing technical support to reduce users' secondary processing of the acquired environmental data;

[0057] 3. In addition, the present application first matches an initial data model that covers a wider range of environmental data, and then further matches a target data model based on the initial data model, and finally only presents the target data model to the user, so that the environmental data included in the target data model obtained by the user is the environmental data he needs, further reducing the probability of the user performing secondary processing on the acquired environmental data, thereby improving the utilization rate of the environmental data presented to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of an exemplary operating environment in which embodiments of the present application can be implemented is shown.

[0059] Figure 2 A flowchart of a method for presenting extended information of a monitoring device according to an embodiment of the present application is shown.

[0060] Figure 3 A block diagram of an information expansion presentation device for monitoring equipment according to an embodiment of the present application is shown.

[0061] Explanation of the accompanying drawings: 10. Monitoring device; 20. Cloud; 21. Data determination module; 22. Data processing module; 23. Data generation module; 30. User terminal. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0064] Figure 1 A schematic diagram of an exemplary operating environment in which the embodiments of the present application can be implemented is shown, and the operating environment includes a monitoring device 10, a cloud 20, and a user terminal 30. Among them, one cloud 20 can be wirelessly connected with multiple monitoring devices 10, and the specific wireless communication method can be based on a wide-area Internet of Things system such as a 4G network or a 5G network, or it can be based on a local Internet of Things.

[0065] The monitoring device 10 can be a single device for collecting environmental data, such as a rainfall sensor, a water level meter, a radar water level meter, etc.; or it can be a sensor network composed of multiple devices, such as an Internet of Things environmental data collection system that can simultaneously monitor multiple environmental factors such as atmospheric temperature, atmospheric humidity, soil temperature, soil humidity, rainfall, wind speed, wind direction, air pressure, radiation, and illumination. In this example, there are multiple monitoring devices 10, and multiple monitoring devices 10 upload the collected environmental data to the cloud 20.

[0066] The cloud 20 is composed of one or more servers. The cloud 20 can receive environmental data uploaded by multiple monitoring devices 10, and establish a data model that is the same as the environment where the monitoring device 10 is located based on the environmental data. For example, the cloud 20 can communicate with the Geographic Information System (GIS), obtain geographic information from the GIS system, and then use digital twin technology to use geographic information and environmental data to establish a data model that is the same as the environment where the monitoring device 10 is located. Specifically, the cloud 20 can first divide the environment where the monitoring device 10 is located into multiple areas, then establish a data model for each area, and finally combine multiple data models into a database. It should be noted that after obtaining the environmental data uploaded by the monitoring device 10, the cloud 20 dynamically updates the corresponding data model.

[0067] The user terminal 30 is a smart device such as a mobile phone, a tablet, a computer, etc. The user terminal 30 is used for the user to request the required environmental data from the cloud 20. Specifically, the user can be a tester in the field environment, or a manager with the authority to manage environmental data. Among them, the tester has the authority to monitor the same type of environmental data in all areas, and the manager has different management permissions, so the cloud 20 of this application first divides it into the first level, the second level and the third level according to the management permissions of the manager. Among them, the first-level manager governs the environmental data in all areas, so the first-level manager can view the environmental data of all areas; the second-level manager governs the environmental data in one area, so the second-level manager can view the environmental data of the area under his jurisdiction; the third-level manager governs the environmental data in one of the locations in the area, so the third-level manager can view the environmental data of the location point under his jurisdiction. In other words, users of different identities are set with corresponding query permissions, so when the user requests to query environmental data, the cloud 20 will return the corresponding environmental data according to the query permissions of the user.

[0068] In order to facilitate the explanation of the process in which the cloud 20 specifically returns the required environmental data to the user terminal 30 , the present application proposes an information expansion presentation method for monitoring equipment. Figure 2 A flowchart of a method for presenting extended information of a monitoring device according to an embodiment of the present application is shown, and the main process of the method is described as follows.

[0069] Step S100: determine the target data model according to the request instruction.

[0070] In a specific example, the request instruction includes element information and identity information.

[0071] The element information includes data such as request time, geographic location, and request type. The request time is the time when the user terminal 30 requests to obtain environmental data. The geographic location is the address in the physical space where the user terminal 30 is located. The request type is the type of environmental data required by the user terminal 30, such as rainfall environmental data, temperature environmental data, and geological environmental data.

[0072] The identity information includes data such as device code, user number and user role. Among them, the device code is a code set by the cloud 20 to distinguish different user terminals 30, and each user terminal 30 has a unique code. The user number is an identifier set by the cloud 20 to facilitate the acquisition of the user's query authority, so the user number can be the user's name, and in order to avoid the difficulty in determining the query authority when there are users with duplicate names, the user number can also be set to a numerical value. User roles include monitoring personnel and management personnel. Monitoring personnel refers to personnel who mainly perform monitoring work on site, and management personnel refers to personnel with remote jurisdiction authority. User roles can be bound to user numbers to avoid confusion during cloud 20 management. For example, the user number of monitoring personnel can start with "00_", and the user number of management personnel can start with "01_".

[0073] Therefore, when the user terminal 30 sends a request instruction to the cloud 20, the cloud 20 will obtain data such as the request time, geographic location, request type, device code, user number, and user role.

[0074] In a specific example, the operation flow after the cloud 20 obtains the request instruction is:

[0075] Step S110: Determine the authenticity of the user terminal 30 according to the request time and the device code. For example, if the user terminal 30 requests to obtain environmental data during non-working hours, the user terminal 30 is considered to be a malicious request, and the user terminal 30 will be prohibited from continuing to access the cloud 20, and the cloud 20 will also stop returning the environmental data required by the user terminal 30. Since the device code of the user terminal 30 is stored in the cloud 20, after the cloud 20 receives the device code, it will determine whether the device code exists in the cloud 20. If the device code does not exist in the cloud 20, the user terminal 30 is considered to be an intrusion user terminal 30, and the user terminal 30 will be prohibited from continuing to access the cloud 20, and the cloud 20 will also stop returning the environmental data required by the user terminal 30.

[0076] Step S120, for the user terminal 30 whose authenticity has been authenticated, the cloud 20 will determine the initial data model according to the geographical location. Specifically, first query the database for the data model corresponding to the geographical location according to the geographical location. The data model corresponding to the geographical location refers to: a data model generated for the environmental data of the on-site environment of the geographical location. Then, the data model corresponding to the geographical location is used as the initial data model. It should be noted that the initial data model includes all types of environmental data of the on-site environment of the geographical location. For example, if the geographical location is A, the initial data model of the geographical location A includes various types of environmental data such as terrain, landform, geology, climate, meteorology, hydrology, vegetation, biodiversity, etc. at the geographical location A.

[0077] Step S130, obtain the target data model according to the user number and the initial data model. First, after determining the initial data model, match the query authority corresponding to the user number according to the user number, remove the environmental data that the user has no right to query in the initial data model according to the query authority, and use the remaining environmental data that the user can query as the target data model.

[0078] It can be seen that, for the obtained request instruction, the cloud 20 first matches an initial data model that covers a larger environmental data surface, and then, based on the initial data model and according to the user's query permissions, retains the environmental data that the user has query permissions to obtain the target data model.

[0079] Step S200: determining an initial presentation mode according to a request instruction.

[0080] First, a presentation mode is determined according to the user role in the identity information. In this example, since the tester is performing maintenance or testing on site, he only needs to obtain environmental data related to the equipment he is responsible for maintenance. Other irrelevant data will interfere with the maintenance personnel's maintenance of the equipment he is responsible for, and the management personnel usually monitor the site remotely, so the management personnel need to control the overall situation, and global control requires multiple types of environmental data to support it. The lack of one of the environmental data may lead to errors in control and cause greater losses. Therefore, the tester's sensitivity to data is higher than that of the management personnel, and the management personnel's sensitivity to images is higher than that of the tester, so the presentation method for the tester in this application can be selected in the chart format, and the chart format also includes bar charts, line charts, scatter charts and bar charts. Since the display effects of bar charts and bar charts are similar, the bar charts and bar charts are placed in the same level. For management personnel, the corresponding presentation method can be selected in the three-dimensional model format or image format. Therefore, after determining the user role, the corresponding presentation mode can be matched for the user role.

[0081] Step S300: determine a final presentation mode according to the target data model, and use the final presentation mode to present the target data model to obtain a presentation result.

[0082] Since the user roles of this application are divided into two types: testers and managers, the final presentation mode obtained for different user roles will also be different. Specifically:

[0083] Step S310: If the user role is a tester, the target data model is input into the neural network model to obtain a final presentation mode, and the target data model is presented using the final presentation mode to obtain a presentation result.

[0084] This application establishes a neural network model in advance, and the process of establishing the neural network model is as follows:

[0085] 1) Data preprocessing. The data preprocessing stage is to standardize the input environmental data sets to ensure that each environmental data set has a similar scale and distribution. An environmental data set refers to a data set composed of environmental data of the same type. In this embodiment, a mean normalization standardization method is adopted, that is, for each environmental data set, the mean and standard deviation of the environmental data set are calculated, and then the value of each feature is subtracted from the mean and divided by the standard deviation to achieve standardization.

[0086] 2) Initialize the weights and bias parameters of the neural network. The Xavier initialization method is used to initialize the weights and bias parameters. The Xavier initialization method can adaptively adjust the initial values ​​of the parameters according to the number of neurons in the previous layer and the number of neurons in the next layer, which helps to avoid the gradient vanishing or gradient exploding problem.

[0087] 3) Define the structure of the neural network, including the input layer, hidden layer, and output layer. This example uses a feedforward neural network with two hidden layers and one output layer, and each layer of the network has multiple neurons. The number of neurons in the output layer is defined as 3 according to the multi-classification labeling in the chart format, corresponding to the three categories of primary, secondary, and tertiary, and the primary level has the highest correlation and is suitable for line charts; the secondary level has a lower correlation than the primary level but higher than the tertiary level, and is suitable for bar charts or bar graphs; the tertiary level has a lower correlation than the secondary level, and is suitable for scatter plots.

[0088] 4) Select the activation function. In this example, ReLU (Rectified Linear Unit) is used as the activation function on each neuron in the hidden layer and the output layer. The ReLU function is a linear function in the positive range and can provide nonlinear transformations to effectively capture the complexity of the input data. The output layer uses the softmax activation function, which can easily obtain the probability prediction for each category.

[0089] 5) Define a loss function to measure the difference between the model's output and the actual label. This example uses the cross entropy loss function. The cross entropy loss function is widely used in multi-classification problems and can effectively measure the distance between the model's prediction and the actual label.

[0090] 6) Repeat the above training process until the model converges or reaches a predetermined number of training rounds. In this embodiment, the Adam optimization algorithm is used to train the model. The Adam algorithm combines the characteristics of the momentum method and the adaptive learning rate, which can accelerate the convergence speed and improve the performance of the model.

[0091] Based on the neural network model established above, the target data model is input into the neural network model, and the neural network model calculates the correlation between the environmental data included in the target data model. For example, the rainfall at the site will affect the river water level at the site, while the rainfall at the site and the oxygen content at the site have no effect on each other, so the rainfall and the river water level have a higher correlation, while the rainfall and the oxygen content have a lower correlation, or even no correlation. In this example, the degree of correlation of the environmental data can be assigned a corresponding score by the neural network model. In other words, the neural network model can calculate the correlation between all environmental data included in the target data model.

[0092] In a specific example, the neural network model can match the classification level according to the ratio of the number of environmental data with association relationships to all environmental data included in the target data model. The association relationship means that the similarity between two environmental data or more than two environmental data is not equal to zero. For example, the neural network model receives the target data model a, which includes 20 environmental data, and among the 20 environmental data, there are 10 environmental data with association, then the ratio of environmental data with association to all environmental data is 50%. If the classification levels stored in the neural network model are 0-30% for the third level, 31%-60% for the second level, and 61%-100% for the first level, since 50% falls into 31%-60%, the target data model a belongs to the second level, and the matching result is a bar chart or a bar chart.

[0093] In another specific example, the neural network model can also match the classification level according to the similarity score of the environmental data with associated relationship among all the environmental data included in the target data model. For example, the neural network model receives the target data model b, which includes 10 environmental data. Among the 10 environmental data, 6 environmental data are interrelated and have a similarity score of 80 points. If the classification levels stored in the neural network model have three levels of 0-30 points, two levels of 31-60 points, and one level of 61 points and above, since 80 points falls within the range of 61 points and above, the target data model b belongs to the first level, and the matching result is a line graph.

[0094] In other examples, the neural network model can also match the classification level based on the ratio of the number of environmental data with associated relationships to all environmental data included in the target data model, the similarity score of environmental data with associated relationships, etc. For example, the neural network model receives the target data model c and the target data model d. The target data model c includes 10 environmental data, 5 of which are associated with each other and the associated score is 50 points; the target data model d includes 25 environmental data, 5 of which are associated with each other and the associated score is 60 points. Then, the final score of the target data model c can be calculated as C = (5 / 10) * 100 + 50 = 100 points, and the final score of the target data model d can be calculated as D = (5 / 25) * 100 + 60 = 80 points. Suppose the classification levels stored in the neural network model are 0-40 points for three levels, 41-80 points for two levels, and 81 points and above for one level. Since 100 points falls into the range of 81 points and above, the target data model c belongs to the first level, and the matching result is a line graph. 80 points falls into the range of 41-80 points, so the target data model d belongs to the second level, and the matching result is a bar graph or a bar chart.

[0095] It should be noted that the above formula for calculating the final scores of the target data model c and the target data model d is customized, and this application does not limit it, and the calculation formula can be adjusted as needed. It should also be noted that in actual application, the specific method used by the neural network model to match the corresponding classification level for the target data model is not limited by this application.

[0096] In the present application, after matching the classification level for the target data model through the neural network model, the presentation mode corresponding to the classification level is adopted as the final presentation mode, and the final presentation mode is used to present the target data model.

[0097] It should be noted that the purpose of selecting the final presentation mode according to the correlation between the environmental data included in the target data model in the present application is: if the correlation of the environmental data included in the target data model is higher, then a chart format that can clearly present the correlated environmental data is selected, such as a line chart, through which the intersection, separation and overlap of multiple environmental data can be seen; if the correlation of the environmental data included in the target data model is lower, then a chart format that can clearly see the trend under the same time length is selected, such as a bar chart or a bar chart, through which the growth trend of multiple environmental data over the same time length can be seen; if the correlation of the environmental data in the target data model is the lowest, then a chart format that can intuitively view the changes in each type of environmental data is selected, such as a scatter plot, through which the distribution of each type of environmental data can be viewed.

[0098] Step S320: If the user role is a manager, the target data model is input into the early warning model to obtain a final presentation mode, and the target data model is presented using the final presentation mode to obtain a presentation result.

[0099] This application also establishes an early warning model in advance. The process of establishing the early warning model is: for each type of environmental data in each area, a corresponding warning value or warning range is set. If the environmental data exceeds the corresponding warning value or warning range, it means that there is an abnormality in the on-site environment.

[0100] Therefore, after receiving the environmental data, the early warning model determines whether the environmental data exceeds the corresponding early warning value or early warning range. If so, the image format is determined as the final presentation mode. This is because when the on-site environment is abnormal, the manager needs to obtain the on-site image in real time, and then use the specific situation displayed in the on-site image as the basis for guiding on-site operations. If not, it means that there is no abnormality in the on-site environment. At this time, the three-dimensional model format is used as the final presentation mode, which is convenient for managers to observe and count the on-site environmental data.

[0101] It should be noted that, in the case where the three-dimensional model format is used as the final presentation mode, the present application will also count the sudden changes in environmental data, such as the sudden increase or decrease in environmental data, and display the sudden changes in environmental data in the form of a line graph, that is, in addition to displaying the environmental data of the three-dimensional model on the user terminal 30, the sudden changes in data are also displayed in a line graph, and the line graph can be located on the display edge of the environmental data of the three-dimensional model to avoid the line graph covering the environmental data of the three-dimensional model. Of course, other display methods can also be used to highlight the sudden changes in data, so that managers can pay attention to the sudden changes in environmental data in a timely manner, so as to take timely precautions.

[0102] Figure 3 A block diagram of an information expansion presentation device for monitoring equipment according to an embodiment of the present application is shown. The device includes a data determination module 21, a data processing module 22 and a data generation module 23.

[0103] The data determination module 21 is used to determine the target data model and the initial presentation mode according to the request instruction, the request instruction includes the user role, the user role includes the tester and the manager, the tester and the manager correspond to different presentation modes respectively, and the target data model includes multiple types of environmental data;

[0104] The data processing module 22 is used to input the target data model into the neural network model to obtain a final presentation mode when the user role is a tester, and the final presentation mode is a presentation mode in the initial presentation mode; it is also used to obtain a final presentation mode according to multiple types of environmental data in the target data model and the warning value corresponding to each type of environmental data when the user role is a manager:

[0105] The data generation module 23 is used to present the target data model in a final presentation mode to obtain a presentation result.

[0106] The modules involved in the embodiments described in the present application may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a data determination module 21, a data processing module 22, and a data generation module 23. The names of these modules do not constitute a limitation on the modules themselves in some cases, for example, the data determination module 21 may also be described as "a module for determining a target data model and an initial presentation mode according to a request instruction".

[0107] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0108] In order to better execute the program of the above method, the present application also provides an electronic device, which includes a memory and a processor.

[0109] The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the above-mentioned information expansion presentation method for monitoring equipment, etc.; the data storage area may store data involved in the above-mentioned information expansion presentation method for monitoring equipment, etc.

[0110] The processor may include one or more processing cores. The processor calls the data stored in the memory by running or executing the instructions, programs, code sets or instruction sets stored in the memory, performs various functions of the present application and processes data. The processor may be at least one of a special purpose integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field programmable gate array, a central processing unit, a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor function can also be other, and the embodiments of the present application are not specifically limited.

[0111] The present application also provides a computer-readable storage medium, for example, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes. The computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned information expansion presentation method for monitoring equipment.

[0112] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) by each other to form a technical solution.

Claims

1. A method for presenting information expansion for monitoring equipment, characterized in that: include: Determine a target data model and an initial presentation mode according to a request instruction, wherein the request instruction includes a user role, the user role includes a tester and a manager, the tester and the manager correspond to different presentation modes respectively, the target data model includes multiple types of environmental data, and the initial presentation mode includes a line chart, a bar chart, and a scatter chart; When the user role is a tester, the target data model is input into the neural network model to obtain a final presentation mode, including: calculating the association degree of multiple types of environmental data included in the target data model, including: obtaining the association degree according to the number of environmental data with an associated relationship among all environmental data included in the target data model; and / or obtaining the association degree according to the similarity score of environmental data with an associated relationship among all environmental data included in the target data model, wherein the associated relationship refers to that the similarity between two environmental data or between multiple environmental data is not equal to zero; obtaining a classification level according to the association degree, wherein one classification level corresponds to one initial presentation mode; determining the initial presentation mode corresponding to the classification level as the final presentation mode; and the final presentation mode is one presentation mode among the initial presentation modes; When the user role is a manager, a final presentation mode is obtained according to multiple types of environmental data in the target data model and the warning values ​​corresponding to each type of environmental data; The target data model is presented using the final presentation mode to obtain a presentation result.

2. The information expansion presentation method for monitoring equipment according to claim 1, characterized in that: The determining of the initial presentation mode according to the request instruction includes: A presentation mode corresponding to the user role is determined, and the presentation mode corresponding to the user role is marked as an initial presentation mode.

3. The information expansion presentation method for monitoring equipment according to claim 1, characterized in that: The initial presentation mode includes a three-dimensional model format and an image format; When the user role is a manager, the final presentation mode is obtained according to the multiple types of environmental data in the target data model and the warning values ​​corresponding to each type of environmental data, including: Determine whether any type of environmental data among multiple types of environmental data exceeds a corresponding warning value or warning range; If so, the image format is determined as the final presentation mode; If not, the three-dimensional model format is determined as the final presentation format.

4. The information expansion presentation method for monitoring equipment according to claim 3, characterized in that: After presenting the target data model in the final presentation mode to obtain a presentation result, the method further includes: Determining the mutated environmental data according to the environmental data included in the target data model; Display environmental data for the mutation.

5. The information expansion presentation method for monitoring equipment according to claim 1, characterized in that: The request instruction includes a geographic location and a user number, and the user number corresponds to the query authority; Determining the target data model according to the request instruction includes: determining an initial data model according to the geographic location; The target data model is determined according to the initial data model and the query authority corresponding to the user number.

6. The method for displaying extended information of a monitoring device according to claim 1, characterized in that: Before determining the target data model and the initial presentation mode according to the request instruction, the method further includes: The request instruction includes the request time and device code; Determining the authenticity of the user terminal (30) based on the request time and the device code; When the user terminal (30) is determined to be authentic, a target data model and an initial presentation mode are determined according to the request instruction; When the user terminal (30) determines that it does not have authenticity, the user terminal (30) is prohibited from accessing.

7. An information expansion presentation device for monitoring equipment, characterized in that: include: A data determination module (21) is used to determine a target data model and an initial presentation mode according to a request instruction, wherein the request instruction includes a user role, the user role includes a tester and a manager, the tester and the manager correspond to different presentation modes respectively, the target data model includes multiple types of environmental data, and the initial presentation mode includes a line chart, a bar chart, and a scatter plot; A data processing module (22) is used for, when the user role is a tester, inputting the target data model into a neural network model to obtain a final presentation mode, comprising: calculating the association degree of multiple types of environmental data included in the target data model, comprising: obtaining the association degree based on the number of environmental data with an association relationship among all the environmental data included in the target data model; and / or obtaining the association degree based on the similarity score of environmental data with an association relationship among all the environmental data included in the target data model, wherein the association relationship refers to that the similarity between two environmental data or between multiple environmental data is not equal to zero; obtaining a classification level based on the association degree, wherein each classification level corresponds to one of the initial presentation modes; determining the initial presentation mode corresponding to the classification level as the final presentation mode; and the final presentation mode is one of the presentation modes in the initial presentation modes; and further used for, when the user role is a manager, obtaining the final presentation mode based on multiple types of environmental data in the target data model and the warning value corresponding to each type of environmental data: The data generation module (23) is used to present the target data model using the final presentation mode to obtain a presentation result.

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