Iot-based platform device low-code construction generation method and system

Abstract

The application belongs to the technical field of device low code construction, and specifically discloses a platform device low code construction generation method and system based on the Internet of Things. The method comprises the following steps: extracting camera installation information in a specified monitoring area and each monitoring image corresponding to each installed camera; when a certain installed camera needs to be monitored and regulated, the installed camera is recorded as a target camera, and the regulation information of the target camera is analyzed; according to the regulation information of the target camera, camera regulation code matching analysis is performed, the regulation code program package corresponding to each regulation parameter of the target camera is obtained, and program replacement is performed. The application effectively solves the problem that the current low code construction is limited and fixed in the application scene, improves the flexibility and pertinence of the development of the monitoring and regulation type business program, avoids the limitation of the current low code construction on the subject requirement of the corresponding object, and meets the low code construction requirement of different types of cameras.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of device low code construction, and relates to a platform device low code construction generation method and system based on the Internet of Things. BACKGROUND

[0002] With the development of the Internet of Things technology, monitoring is increasingly widely used in fire protection, construction, energy, agriculture, smart cities and the like. However, the underlying technology of monitoring is relatively complex, and this part of underlying code is in a shielded state for developers for the protection of technology, which is inconvenient for developers to develop individually according to the monitoring application. Therefore, a low code construction method needs to be designed.

[0003] The current code method encapsulates the target according to the obtained device description information, and then generates a low code platform through individual configuration. It is mainly applicable to the code configuration of a single device or the same type of device. Obviously, there are still the following problems: 1. The quality requirement of monitoring type Internet of Things devices for monitoring is high. The current low code construction for monitoring type is biased towards push type and statistical type, and the applicable scene is relatively limited and fixed. It belongs to a rough type of business program development, and there is still a certain lack of flexibility.

[0004] 2. It cannot realize low code construction of different types of cameras, and cannot meet the low code construction needs of different types of cameras.

[0005] It cannot serve the fine business program development. The current camera regulation still needs manual control, and there is no adaptive low code program, which is relatively cumbersome, and cannot guarantee the monitoring effect of the camera and the timeliness of the camera monitoring adjustment. SUMMARY

[0006] In view of this, in order to solve the problems raised in the background art, the present application proposes a platform device low code construction generation method and system based on the Internet of Things.

[0007] The purpose of the application can be achieved through the following technical solutions: the first aspect of the application provides a platform device low code construction generation method based on the Internet of Things, which comprises the following steps: step 1, device information extraction: extracting camera installation information in a specified monitoring area.

[0008] Step 2, camera monitoring information extraction: extracting each monitoring image corresponding to each installed camera in the specified monitoring area.

[0009] Step 3, camera monitoring information analysis: analyzing the monitoring and regulation needs of each installed camera, and when a certain installed camera needs monitoring and regulation, recording the installed camera as a target camera, and analyzing the regulation information of the target camera.

[0010] Step 4, low code matching setting: according to the regulation information of the target camera, the camera regulation code matching analysis is carried out, and the regulation code program package corresponding to each regulation parameter of the target camera is obtained.

[0011] Step 5, low code setting replacement: the source monitoring code of the target camera is extracted from the cloud storage, and the source monitoring code is replaced according to the regulation code program package corresponding to each regulation parameter of the target camera.

[0012] Preferably, the camera installation information includes initial setting parameters and code numbers corresponding to each installed camera, wherein the initial setting parameters include initial focal length value, initial shooting rotation speed and initial shooting angle.

[0013] Preferably, the monitoring regulation demand analysis of each installed camera includes: locating the definition, contrast and brightness of each installed camera from the corresponding monitoring image as the image quality parameters, and then calling the corresponding configured image quality parameters of each installed camera from the cloud storage.

[0014] Calculate the image quality level deviation index λ1 of each installed camera monitoring image i , i represents the camera code number, i = 1, 2, ……n.

[0015] Locate the attributes, categories and outlines of the monitoring objects from the corresponding monitoring images of each installed camera, analyze the image content level deviation index of each installed camera monitoring image, and record it as λ2 i .

[0016] Calculate the image monitoring deviation comprehensive evaluation index γ i of each installed camera

[0017]

[0018] Wherein, e is a natural constant, and ε1, ε2 are respectively the set image quality level and image content level corresponding image monitoring deviation comprehensive evaluation proportion weight factor.

[0019] Compare the image monitoring deviation comprehensive evaluation index of each installed camera with its set value, if the image monitoring deviation comprehensive evaluation index of a certain installed camera is greater than or equal to its set value, it is judged that the installed camera needs monitoring and regulation, otherwise it is judged that the installed camera does not need monitoring and regulation.

[0020] Preferably, the calculation of the image quality level deviation index of each installed camera monitoring image includes: counting the number of monitoring quality deviation images M i , the number of monitoring deviation quality parameters C i and the maximum deviation value P of monitoring quality parameters of each installed camera.i .

[0021] calculating image quality level deviation index λ1 of each installed camera i ,

[0022]

[0023] wherein a1, a2, a3 are respectively set quality deviation image number, monitoring deviation quality parameter number, monitoring quality parameter maximum deviation value corresponding image quality level deviation evaluation proportion weight, M', C', P' are respectively set reference quality deviation image number, monitoring deviation quality parameter number, monitoring quality parameter maximum deviation value, is a set image quality level deviation evaluation correction factor.

[0024] Preferably, the analysis of the image content level deviation index of each installed camera monitoring image includes: if the attribute of the monitoring object in the monitoring image corresponding to a certain installed camera is a variable object, the monitoring image is recorded as a target image.

[0025] extracting the category and contour of the monitoring object in the target image, and then counting the contour overlap degree of the target image and the monitoring offset distance L.

[0026] calculating the image content level deviation index λ2' of the monitoring image of the installed camera,

[0027]

[0028] wherein a4, a5 are respectively the contour overlap degree and the monitoring offset distance corresponding image content level deviation evaluation proportion weight, L' is respectively the set reference image overlap degree and image monitoring offset distance, is a set image content level deviation evaluation correction factor.

[0029] If the attribute of the monitoring object in each monitoring image corresponding to a certain installed camera is a fixed object, the image content level deviation index of the monitoring image corresponding to the camera is recorded as λ2".

[0030] Thus, the image content level deviation index λ2 of each installed camera monitoring image is obtained i , λ2 i takes the value of λ2' or λ2".

[0031] Preferably, the analysis of the target camera's control information includes: comparing the target camera's monitoring image quality level deviation index and the target camera's monitoring image content level deviation index with their set values respectively, and determining the control type of the target camera.

[0032] If the regulation type of the target camera is image quality, the regulation parameters of image quality deviation, i.e. focal length and shooting rotation speed, are extracted from the cloud storage, and the regulation parameters and regulation value interval of the target camera corresponding to the image quality level are confirmed, and then the regulation information of the target camera is obtained.

[0033] If the regulation type of the target camera is image content or image quality and image content, the regulation parameters and regulation values are confirmed in the same way as the confirmation of the image quality.

[0034] Preferably, the regulation parameters of the target camera corresponding to the image quality level are confirmed, and the specific confirmation process is as follows: set each focal length experimental group, and extract the set focal length value of each focal length experimental group.

[0035] Extract the collected images of the target camera corresponding to each focal length experimental group, analyze the image quality deviation index ψdof each focal length experimental group r , r represents the number of focal length experimental groups, r = 1, 2, …… u.

[0036] Set each rotation speed experimental group, and analyze the image quality deviation index ψ'dof each rotation speed experimental group in the same way as the analysis of the image quality deviation index of each focal length experimental group, d represents the number of rotation speed experimental groups, d = 1, 2, …… v.

[0037] Extract the image quality level deviation index λ1 i of the target camera.

[0038] If λ1 i -min(ψ r ) ≥ Δλ0, it is determined that the regulation parameter of the target camera corresponding to the image quality level is the focal length, min(ψ r ) is the minimum value of the image quality deviation index of each focal length experimental group, and Δλ0is the set first reference deviation index difference.

[0039] If λ1 i -min(ψ'd d ) ≥ Δλ0, it is determined that the regulation parameter of the target camera corresponding to the image quality level is the shooting rotation speed, min(ψ'd d ) is the minimum value of the image quality deviation index of each rotation speed experimental group.

[0040] If λ1 i -min(ψ r ) ≥ Δλ0and λ1 i -min(ψ'd d ) ≥ Δλ0, it is determined that the regulation parameter of the target camera corresponding to the image quality level is the focal length and the shooting rotation speed.

[0041] Preferably, the confirming the target camera corresponding image quality level corresponding to the regulation value interval of the regulation parameter comprises: if the regulation parameter of the target camera corresponding image quality level is the focal length.

[0042] Statistically, the image quality deviation index difference of each focal length experimental group is compared with the set second reference deviation index difference, and each focal length experimental group greater than the second reference deviation index difference is selected as each reference focal length experimental group. r , Δλr=λ1i -ψ r.

[0043] Statistically, the image quality deviation index difference of each focal length experimental group is compared with the set second reference deviation index difference, and each focal length experimental group greater than the second reference deviation index difference is selected as each reference focal length experimental group.

[0044] The set focal length value of each reference focal length experimental group is extracted, and the maximum set and minimum set focal length values are selected therefrom to form the regulation value interval of the focal length.

[0045] If the regulation parameter of the target camera corresponding image quality level is the shooting rotation speed, the regulation value interval of the shooting rotation speed is obtained by analyzing the regulation value interval of the focal length in the same way.

[0046] If the regulation parameter of the target camera corresponding image quality level is the focal length and the shooting rotation speed, the regulation value intervals of the focal length and the shooting rotation speed are analyzed in turn.

[0047] Preferably, the camera regulation code matching analysis comprises: if the regulation parameter of the target camera is the focal length, the regulation value interval of each regulation code program package corresponding to the focal length is located from the cloud storage library.

[0048] The regulation interval overlap degree corresponding to each regulation code program package is counted.

[0049] The regulation matching degree of each regulation code program package is calculated by the formula The regulation matching degree of each regulation code program package is calculated by the formula

[0050] If the regulation parameter of the target camera is the rotation speed or the shooting angle, the regulation code program package of the rotation speed and the shooting angle is obtained by analyzing the target regulation code program package of the focal length in the same way.

[0051] The second aspect of the present application provides a platform device low-code construction generation system based on the Internet of Things, comprising: a code construction information extraction module for extracting camera installation information in a specified monitoring area and each monitoring image corresponding to each installed camera.

[0052] The camera monitoring analysis module is configured to analyze the monitoring images corresponding to each camera, obtain monitoring and control requirements corresponding to each camera, and when a certain camera needs monitoring and control, mark the camera as a target camera and analyze the control information.

[0053] The low-code matching setting module is configured to perform camera control code matching analysis to obtain a control code program package of each control parameter corresponding to the target camera.

[0054] The low-code setting replacement module is configured to extract the source monitoring code of the target camera from the cloud storage, and replace the source monitoring code according to the control code program package of each control parameter corresponding to the target camera.

[0055] The cloud storage is configured to store the source monitoring code of each camera and the configured image quality parameters, store the control value interval of each control code program package corresponding to each control parameter, store the reference contour corresponding to each monitoring object category, and store each control parameter corresponding to the image quality deviation and the image content deviation.

[0056] Compared with the prior art, the present application has the following advantages: (1) The present application confirms the control parameters and control intervals when a certain camera needs monitoring and control, and then performs camera control code matching analysis, effectively solving the problem that the current low-code construction is limited and fixed in application scenarios, improving the flexibility and pertinence of the development of monitoring and control business programs, avoiding the limitations of the current low-code construction corresponding to the object subject requirements, and meeting the low-code construction requirements of different types of cameras.

[0057] (2) The present application constructs a control low code for a demand control camera, makes up for the deficiency that the current low-code construction method cannot serve the control of fine business program development, realizes the autonomous control of the demand control camera, and thus avoids the tediousness of the current manual control method, and effectively ensures the monitoring effect of the camera and the timeliness of the monitoring adjustment from another aspect.

[0058] The present application analyzes the monitoring and control requirements of each camera from the image quality level and the image content level, displays the monitoring state of each camera, realizes multi-dimensional analysis of the monitoring and control requirements of each camera, effectively improves the rationality and reliability of the camera control analysis results, and thus ensures the timeliness of the camera monitoring abnormality awareness and the timeliness of the processing. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description only some of the embodiments of the present application, and for those skilled in the art, without creative labor, can also obtain other drawings from these drawings.

[0060] Figure 1 The method of the present application is implemented by the step flow diagram.

[0061] Figure 2 The schematic diagram of the connection of each module of the system of the present application. DETAILED DESCRIPTION

[0062] The technical solutions of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] Please refer to Figure 1 The first aspect of the present application provides a platform device low code construction generation method based on Internet of Things, comprising: step 1, device information extraction: extracting camera installation information in the specified monitoring area.

[0064] Specifically, the camera installation information includes initial setting parameters and code numbers corresponding to each installed camera, wherein the initial setting parameters include initial focal length value, initial shooting rotation speed and initial shooting angle.

[0065] Step 2, camera monitoring information extraction: extracting each monitoring image corresponding to each installed camera in the specified monitoring area.

[0066] Step 3, camera monitoring information analysis: analyzing the monitoring and control requirements of each installed camera, and when a certain installed camera needs monitoring and control, recording the installed camera as a target camera, and analyzing the control information of the target camera.

[0067] Exemplarily, the monitoring and control requirement analysis of each installed camera comprises: D1, locating the definition, contrast and brightness from each monitoring image corresponding to each installed camera as image quality parameters, and then retrieving each image quality parameter corresponding to each installed camera configuration from the cloud storage.

[0068] D2, calculating the image quality level deviation index λ1 of each installed camera i , i represents the code number of the installed camera, i = 1, 2,..., n.

[0069] Further, the monitoring image quality level deviation index of each installed camera is calculated, including:

[0070] The number of monitoring quality deviation images of each installed camera is counted i The number of monitoring deviation quality parameters of each installed camera is counted i The maximum monitoring quality parameter deviation value of each installed camera is counted i .

[0071] It should be noted that the specific statistical process of the number of monitoring quality deviation images, the number of monitoring deviation quality parameters and the maximum monitoring quality parameter deviation value corresponding to each installed camera is as follows: 1) The number of monitoring quality deviation images of each installed camera: compare each image quality parameter of each monitoring image corresponding to each installed camera with the configured image quality parameter, if the difference between the image quality parameter of the monitoring image corresponding to the installed camera and the configured image quality parameter is greater than the permitted difference value of the image quality parameter, the monitoring image is a monitoring quality deviation image, and thus the number of monitoring quality deviation images of each installed camera is obtained.

[0072] 2) The number of monitoring deviation quality parameters of each installed camera: compare the difference between each image quality parameter of each monitoring image corresponding to each installed camera and the configured image quality parameter with the permitted difference value of each image quality parameter, if the difference between the image quality parameter of the monitoring image corresponding to the installed camera and the configured image quality parameter is greater than the permitted difference value of the image quality parameter, the image quality parameter is determined to be a monitoring deviation quality parameter, and thus the number of monitoring deviation quality parameters corresponding to each installed camera is obtained.

[0073] 3) The maximum monitoring quality parameter deviation value of each installed camera: extract the maximum difference from the difference between each image quality parameter of each monitoring image corresponding to each installed camera and the configured image quality parameter, and take it as the maximum monitoring quality parameter deviation value of each installed camera.

[0074] The monitoring image quality level deviation index λ1 of each installed camera is calculated i ,

[0075]

[0076] wherein e is a natural constant, a1, a2, a3 are respectively the image quality level deviation evaluation proportion weight corresponding to the set number of quality deviation images, the number of monitoring deviation quality parameters, and the maximum monitoring quality parameter deviation value, M', C', P' are respectively the set reference number of quality deviation images, the number of monitoring deviation quality parameters, and the maximum monitoring quality parameter deviation value, is the set image quality level deviation evaluation correction factor.

[0077] D3, locate the attribute, category and contour of the monitoring object from each monitoring image corresponding to each installed camera, analyze the content level deviation index of each installed camera monitoring image, and mark it as λ2 i .

[0078] In one specific embodiment, the attribute corresponding to the monitoring object includes a variable object and a fixed object, where the variable object refers to an object that can move autonomously, and the category includes but is not limited to personnel and animals, and the fixed object refers to an object that cannot move autonomously.

[0079] It should be noted that when the monitoring object is a variable object, unlike the conventional fixed monitoring object, the movement of the variable object will cause certain interference to the monitoring of the camera, and the monitoring method of the fixed object is also difficult to guarantee the monitoring of the variable object, so the attribute analysis of the monitoring object is needed.

[0080] Further, analyzing the content level deviation index of each installed camera monitoring image includes: if the attribute of the monitoring object in a certain monitoring image corresponding to a certain installed camera is a variable object, marking the monitoring image as a target image.

[0081] Extract the category and contour of the monitoring object in the target image, and then calculate the contour overlap degree of the target image And the monitoring offset distance L.

[0082] It should be noted that the contour overlap degree of the contour overlap degree of the target image is: based on the category of the monitoring object in the target image, locate the reference contour corresponding to the category of the monitoring object in the target image from the cloud storage.

[0083] Compare the contour of the monitoring object in the target image with the reference contour of the category of the monitoring object in the target image to obtain the contour overlap area of the monitoring object in the target image.

[0084] Extract the contour area of the monitoring object in the target image, and calculate the contour overlap degree of the target image according to the formula .

[0085] It should also be noted that the specific acquisition process of the monitoring offset distance of the target image is: locating the center point position corresponding to the monitoring object from the target image.

[0086] Extract the center point position of the target image, compare the center point position corresponding to the monitoring object in the target image with the center point position of the target image, obtain the horizontal distance between the center point position of the monitoring object in the target image and the center point position of the target image, and take it as the monitoring offset distance of the target image.

[0087] Calculate the image content level deviation index λ2' of the installed camera,

[0088]

[0089] Wherein, a4, a5 are the profile overlap degree, the image content level deviation evaluation proportion weight corresponding to the monitoring offset distance, L' is the image overlap degree and the image monitoring offset distance set as a reference, The image content level deviation evaluation correction factor set.

[0090] If the attributes of the monitoring object in each monitoring image corresponding to a certain installed camera are all fixed objects, the image content level deviation index of the camera corresponding to the monitoring image is denoted as λ2''.

[0091] Thus, the image content level deviation index λ2 of each installed camera is obtained i , λ2 i The value of λ2 is λ2' or λ2''.

[0092] In one specific embodiment, λ2'' is a constant, and its specific value can be 0.1.

[0093] D4, calculate the image monitoring deviation comprehensive evaluation index γ of each installed camera i ,

[0094]

[0095] Wherein, ε1, ε2 are the image quality level and the image content level corresponding to the image monitoring deviation comprehensive evaluation proportion weight factor set.

[0096] D5, compare the image monitoring deviation comprehensive evaluation index of each installed camera with its set value, if the image monitoring deviation comprehensive evaluation index of a certain installed camera is greater than or equal to its set value, it is determined that the installed camera needs monitoring and control, otherwise it is determined that the installed camera does not need monitoring and control.

[0097] The embodiment of the application analyzes the monitoring and control demand of each installed camera from the image quality level and the image content level, displays the monitoring state of each installed camera, realizes the multi-dimensional analysis of the monitoring and control demand of each installed camera, effectively improves the rationality and reliability of the camera control analysis result, and further ensures the timeliness of the camera monitoring abnormality awareness and the timeliness of the processing.

[0098] Further, the control information of the target camera is analyzed, including: F1, comparing the image quality level deviation index of the target camera and the image content level deviation index of the target camera with their set values respectively, and determining the control type of the target camera.

[0099] It can be understood that the specific determination basis of the regulation type of the target camera is that if the image quality level deviation index of the target camera is greater than the set value, it is determined that the regulation type of the target camera is the image quality level.

[0100] If the image content level deviation index of the target camera is greater than the set value, it is determined that the regulation type of the target camera is the image content level.

[0101] F2, if the regulation type of the target camera is the image quality, the regulation parameters of the image quality deviation are extracted from the cloud storage, which are the focal length and the shooting rotation speed respectively, and the regulation parameters and the regulation value interval of the target camera corresponding to the image quality level are confirmed, and then the regulation information of the target camera is obtained.

[0102] It can be understood that the regulation parameters of the target camera corresponding to the image quality level are confirmed, and the specific confirmation process is that the initial focal length value is taken as the reference, the shooting rotation speed is kept unchanged, each focal length experimental group is set, and the set focal length value of each focal length experimental group is extracted.

[0103] The collected images of the target camera corresponding to each focal length experimental group are extracted, and the image quality deviation index ψdof each focal length experimental group is analyzed in the same way as the analysis mode of the image quality level deviation index of each arrangement camera. r , r represents the number of the focal length experimental group, and r = 1, 2, …… u.

[0104] The initial shooting rotation speed is taken as the reference, the focal length is kept unchanged, each rotation speed experimental group is set, the set shooting rotation speed value of each rotation speed experimental group is extracted, and the image quality deviation index ψ'dof each rotation speed experimental group is obtained in the same way as the analysis mode of the image quality deviation index corresponding to each focal length experimental group, d represents the number of the rotation speed experimental group, and d = 1, 2, …… v.

[0105] The image quality level deviation index λ1 i of the target camera is extracted.

[0106] If λ1 i -min(ψ r ) ≥ Δλ0, it is determined that the regulation parameter of the target camera corresponding to the image quality level is the focal length, min(ψ r ) is the minimum value of the image quality deviation index corresponding to each focal length experimental group, and Δλ0is the set first reference deviation index difference.

[0107] If λ1 i -min(ψ′ d ) ≥ Δλ0, it is determined that the regulation parameter of the target camera corresponding to the image quality level is the shooting rotation speed, min(ψ′d ) is the minimum value in the image quality deviation index corresponding to each rotation speed experimental group.

[0108] If λ1 i -min(ψ r ) ≥ Δλ0and λ1 i -min(ψ′ d ) ≥ Δλ0, it is determined that the control parameter corresponding to the image quality level of the target camera is the focal length and the shooting rotation speed.

[0109] It can also be understood that the control value interval of the control parameter corresponding to the image quality level of the target camera includes: if the control parameter corresponding to the image quality level of the target camera is the focal length.

[0110] The image quality deviation index difference Δλ r r of each focal length experimental group is calculated. -ψ r.

[0111] The image quality deviation index difference of each focal length experimental group is compared with the set second reference deviation index difference, and each focal length experimental group greater than the second reference deviation index difference is selected as each reference focal length experimental group.

[0112] In one specific embodiment, the first reference deviation index difference is greater than the second reference deviation index difference.

[0113] The set focal length value of each reference focal length experimental group is extracted, and the maximum set and minimum set focal length value is selected therefrom to form the control value interval of the focal length.

[0114] If the control parameter corresponding to the image quality level of the target camera is the shooting rotation speed, the control value interval of the shooting rotation speed is analyzed in the same way as the control value interval of the focal length.

[0115] If the control parameter corresponding to the image quality level of the target camera is the focal length and the shooting rotation speed, the control value intervals of the focal length and the shooting rotation speed are analyzed in turn.

[0116] F3, if the control type of the target camera is image content or image quality and image content, the control parameter and control value are determined in the same way as the determination of the image quality.

[0117] It should be noted that each control parameter corresponding to the image content deviation is the focal length and the initial shooting angle.

[0118] Step 4, low code matching setting: according to the control information of the target camera, the camera control code matching analysis is performed to obtain the control code program package corresponding to each control parameter of the target camera.

[0119] Exemplarily, the camera control code matching analysis is performed, including: if the control parameter of the target camera is the focal length, the focal length corresponding to the control value interval of each control code package is located from the cloud storage.

[0120] The overlapping degree of the control interval corresponding to each control code package is counted.

[0121] It should be noted that the specific acquisition process of the overlapping degree of the control interval corresponding to each control code package is that the control value interval corresponding to each control code package and the focal length control value interval of the target camera are marked on the number axis.

[0122] The overlapping length of each control code package and the focal length control value interval is obtained.

[0123] The formula is The overlapping degree of the control interval corresponding to each control code package is obtained by analysis.

[0124] The formula is The control matching degree of each control code package is calculated, and the control code package with the largest control matching degree is selected as the control code package of the focal length.

[0125] If the control parameter of the target camera is the rotation speed or the shooting angle, the analysis method of the target control code package of the focal length is the same, and the control code package of the rotation speed and the shooting angle is obtained.

[0126] Step 5, low code setting replacement: the source monitoring code of the target camera is extracted from the cloud storage, and the source monitoring code is replaced according to the control code package corresponding to each control parameter of the target camera.

[0127] It should be noted that the source monitoring code is replaced, and the specific replacement process is that the source control code package of each control parameter is located from the source monitoring code of the target camera, and the control code package corresponding to each control parameter of the target camera is replaced with the source control code package.

[0128] The embodiment of the application confirms the control parameter and the control interval when a camera needs to be monitored and controlled, and then performs camera control code matching analysis, effectively solves the problem that the current low code construction is limited and fixed in the applicable scene, improves the flexibility and pertinence of the development of the monitoring and control type business program, avoids the limitation of the current low code construction corresponding object subject requirement, and meets the low code construction demand of different types of cameras.

[0129] The embodiment of the present application also compensates for the deficiency of the current low-code construction method that cannot serve the control type refined business program development by regulating the demand regulation type camera, realizes the autonomous regulation of the demand regulation camera, and further avoids the tediousness of the current manual control method, and also effectively ensures the monitoring effect and timeliness of the monitoring regulation of the camera from another aspect.

[0130] Please refer to Figure 2 The second aspect of the present application provides a platform device low-code construction generation system based on the Internet of Things, which comprises a code construction information extraction module, a camera monitoring analysis module, a low-code matching setting module, a low-code setting replacement module and a cloud storage library.

[0131] In the above, the cloud storage library is connected with the camera monitoring analysis module, the low-code matching setting module and the low-code setting replacement module respectively, the camera monitoring analysis module is connected with the code construction information extraction module and the low-code matching setting module respectively, and the low-code setting replacement module is connected with the low-code matching setting module.

[0132] The code construction information extraction module is used to extract camera installation information in a specified monitoring area and each monitoring image corresponding to each installed camera.

[0133] The camera monitoring analysis module is used to analyze the monitoring image corresponding to each installed camera, obtain the monitoring regulation demand corresponding to each installed camera, and record the installed camera as a target camera when the installed camera needs monitoring regulation, and analyze the regulation information.

[0134] The low-code matching setting module is used to perform camera regulation code matching analysis to obtain the regulation code program package of each regulation parameter corresponding to the target camera.

[0135] The low-code setting replacement module is used to extract the source monitoring code of the target camera from the cloud storage library, and replace the source monitoring code according to the regulation code program package of each regulation parameter corresponding to the target camera.

[0136] The cloud storage library is used to store the source monitoring code of each installed camera and the configured image quality parameters, store the regulation value interval of each regulation code program package corresponding to each regulation parameter, store the reference contour corresponding to each monitoring object category, and store each regulation parameter corresponding to the image quality deviation and the image content deviation.

[0137] The above content is only an example and description of the concept of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present application, which shall belong to the protection scope of the present application.

Claims

1. A low-code construction and generation method for IoT-based platform devices, characterized in that: include: Step 1: Equipment Information Extraction: Extract the camera placement information within the specified monitoring area; Step 2, Camera Monitoring Information Extraction: Extract each monitoring image corresponding to each camera installed within the specified monitoring area; Step 3, Camera monitoring information analysis: Analyze the monitoring and control needs of each installed camera, and when a certain installed camera needs monitoring and control, record that installed camera as the target camera and analyze the control information of the target camera; Step 4, Low-code matching settings: Based on the control information of the target camera, perform camera control code matching analysis to obtain the control code program package corresponding to each control parameter of the target camera; Step 5, Low-code setting replacement: Extract the source monitoring code of the target camera from the cloud repository, and replace the source monitoring code according to the control code program package corresponding to each control parameter of the target camera.

2. The low-code construction and generation method for IoT-based platform devices as described in claim 1, characterized in that: The camera placement information includes the initial setting parameters and code number corresponding to each placed camera. The initial setting parameters include the initial focal length, initial shooting rotation speed, and initial shooting angle.

3. The low-code construction and generation method for IoT-based platform devices as described in claim 1, characterized in that: The analysis of the monitoring and control needs for each installed camera includes: Sharpness, contrast and brightness are located from each monitoring image corresponding to each installed camera and used as each image quality parameter. Then, the image quality parameters corresponding to each installed camera are retrieved from the cloud storage. Calculate the deviation index λ1 of the image quality of each installed camera. i , where i represents the camera placement code number, i = 1, 2, ..., n; The attributes, categories, and outlines of monitored objects are located from the monitoring images corresponding to each set of cameras. The content-level deviation index of the monitoring images from each set of cameras is analyzed and denoted as λ2. i ; Calculate the comprehensive evaluation index γ of image monitoring deviation for each installed camera. i , Where e is a natural constant, and ε1 and ε2 are the weighting factors for the comprehensive evaluation of image monitoring deviations at the image quality level and image content level, respectively. The comprehensive evaluation index of image monitoring deviation corresponding to each installed camera is compared with its set value. If the comprehensive evaluation index of image monitoring deviation corresponding to a certain installed camera is greater than or equal to its set value, it is determined that the installed camera needs monitoring and control; otherwise, it is determined that the installed camera does not need monitoring and control.

4. The low-code construction and generation method for IoT-based platform devices as described in claim 3, characterized in that: The calculation of the deviation index of image quality at each installed camera includes: Count the number of images with quality deviations from each installed camera (M). i Number of quality parameters for monitoring deviations (C) i and the maximum deviation value P of the monitored quality parameter i ; Calculate the deviation index λ1 of the image quality of each installed camera. i , Where a1, a2, and a3 represent the weights of the image quality level deviation assessment corresponding to the set number of quality deviation images, the number of monitored deviation quality parameters, and the maximum deviation value of the monitored quality parameters, respectively; and M′, C′, and P′ represent the set number of quality deviation images, the number of monitored deviation quality parameters, and the maximum deviation value of the monitored quality parameters, respectively. A correction factor for evaluating deviations in image quality.

5. The low-code construction and generation method for IoT-based platform devices as described in claim 3, characterized in that: The analysis of the deviation index at the content level of the monitored images from each installed camera includes: If the attribute of the monitored object in a monitoring image corresponding to a certain installed camera is a changing object, then the monitoring image is recorded as the target image; Extract the category and contour of the monitored object from the target image, and then calculate the contour overlap of the target image. and monitoring offset distance L; Calculate the content-level deviation index λ2′ of the monitored images from the installed camera. Among them, a4 and a5 represent the weights of the image content-level deviation assessment corresponding to contour overlap and monitoring offset distance, respectively. L′ represents the image overlap and image monitoring offset distance, respectively, for the set reference. A correction factor for evaluating deviations at the image content level; If the attributes of the monitored objects in each monitoring image corresponding to a certain camera are all fixed objects, then the content-level deviation index of the monitoring image corresponding to that camera is denoted as λ2″. This yields the deviation index λ2 of the images monitored by each installed camera. i ,λ2 i The value can be either λ2′ or λ2″.

6. The low-code construction and generation method for IoT-based platform devices as described in claim 3, characterized in that: The analysis of the target camera's control information includes: The deviation index of image quality and the deviation index of image content monitored by the target camera are compared with their set values ​​to determine the control type of the target camera. If the control type of the target camera is image quality, then the control parameters of the image quality deviation are extracted from the cloud storage, which are focal length and shooting rotation speed, and the control parameters and control value range of the corresponding image quality level of the target camera are confirmed, and then used as the control information of the target camera. If the control type of the target camera is image content or image quality and image content, its control parameters and control values ​​are obtained by confirming them in the same way as the confirmation method of image quality.

7. The low-code construction and generation method for IoT-based platform devices as described in claim 6, characterized in that: The specific confirmation process for the image quality control parameters corresponding to the target camera is as follows: Set up experimental groups for each focal length and extract the set focal length values ​​for each experimental group; Extract the images acquired by the target camera for each focal length experimental group, and analyze the image quality deviation index ψ for each focal length experimental group. r r represents the focal length experimental group number, r = 1, 2, ..., u; Each rotational speed experimental group is set up, and the image quality deviation index ψ′d corresponding to each rotational speed experimental group is obtained by similar analysis according to the analysis method of the image quality deviation index corresponding to each focal length experimental group, where d represents the rotational speed experimental group number, d=1,2,......v; Extract the deviation index λ1 of the target camera's image quality. i ; If λ1 i -min(ψ r If )≥Δλ0, then the control parameter for the image quality level of the target camera is determined to be the focal length, min(ψ r ) represents the minimum value of the image quality deviation index corresponding to each focal length experimental group, and Δλ0 is the set first reference deviation index difference; If λ1 i -min(ψ′ d If )≥Δλ0, then the control parameter for the image quality level of the target camera is determined to be the shooting rotation speed, min(ψ′ d () represents the minimum value among the image quality deviation indices corresponding to each rotational speed experimental group; If λ1 i -min(ψ r )≥Δλ0 and λ1 i -min(ψ′ d If )≥Δλ0, then the control parameters for the image quality level of the target camera are determined to be focal length and shooting rotation speed.

8. The low-code construction and generation method for IoT-based platform devices as described in claim 7, characterized in that: The determination of the control value range of the control parameters corresponding to the image quality level of the target camera includes: If the target camera's image quality control parameter is focal length; Calculate the image quality deviation index difference Δλ for each focal length experimental group. r Δλr=λ1i -ψ r; The image quality deviation index difference corresponding to each focal length experimental group is compared with the set second reference deviation index difference, and each focal length experimental group with a greater than the second reference deviation index difference is selected as the reference focal length experimental group. Extract the set focal length values ​​of each reference focal length experimental group, and select the maximum and minimum set focal length values ​​to form the focal length adjustment range. If the control parameter for the image quality of the target camera is the shooting rotation speed, the control range of the shooting rotation speed can be obtained by similarly analyzing the control range of the focal length. If the control parameters for the image quality of the target camera are focal length and shooting rotation speed, then analyze the control value ranges for focal length and shooting rotation speed in turn.

9. The low-code construction and generation method for IoT-based platform devices as described in claim 8, characterized in that: The aforementioned camera control code matching analysis includes: If the control parameter of the target camera is focal length, locate the control value range of each control code package corresponding to the focal length from the cloud storage repository; Calculate the overlap of the control intervals corresponding to each control code package; Through formula Calculate the control matching degree of each control code package, and select the control code package with the highest control matching degree as the control code package for focal length. If the target camera's control parameters are rotation speed or shooting angle, the control code packages for rotation speed and shooting angle can be obtained by analyzing the target control code package in the same way as the focal length control code package.

10. A low-code build and generation system for IoT-based platform devices, characterized in that: include: The code constructs an information extraction module, which is used to extract camera placement information and corresponding monitoring images within a specified monitoring area; The camera monitoring and analysis module is used to analyze the monitoring images corresponding to each installed camera to obtain the monitoring and control requirements corresponding to each installed camera. When a certain installed camera requires monitoring and control, the installed camera is recorded as the target camera, and the control information is analyzed. The low-code matching setting module is used to perform camera control code matching analysis to obtain the control code program package corresponding to each control parameter of the target camera; The low-code setup replacement module is used to extract the source monitoring code of the target camera from the cloud repository and replace the source monitoring code according to the control code program package corresponding to each control parameter of the target camera. The cloud repository is used to store the source monitoring code and configured image quality parameters of each camera, the control value range of each control parameter corresponding to each control code package, the reference contour corresponding to each monitoring object category, and the control parameters corresponding to image quality deviation and image content deviation.

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