Integrated system of RGB control and infrared night vision control

Abstract

The invention relates to the field of illumination control, and discloses an integrated system of RGB control and infrared night vision control, and the system comprises a scene priority judgment module which is used for evaluating the scene criticality corresponding to an infrared night vision application scene and setting the control priority corresponding to the infrared night vision application scene; the equipment resource selection module is used for calculating a resource regulation and control energy efficiency coefficient corresponding to the equipment resource; the response time measuring and calculating module is used for calculating the response lag time length of the RGB equipment in the scene control resources; the collaborative effect evaluation module is used for analyzing resource characteristic attributes corresponding to the scene control resources and performing resource combination on the collaborative coupling resources to obtain target collaborative resources; and the integrated control execution module is used for setting an integrated control scheme corresponding to the target collaborative resource, and executing the infrared night vision integrated operation of the RGB equipment by using the target collaborative resource to obtain a control result. According to the invention, the collaboration of RGB control and infrared night vision control can be improved.

Description

Technical Field

[0001] The invention relates to the field of lighting control, and in particular to an integrated system of RGB control and infrared night vision control. Background Art

[0002] In many fields, such as security monitoring, smart home, photography and videography, the coordinated use of RGB control and infrared night vision control is crucial. RGB control can achieve precise adjustment of colors to create a rich and diverse visual atmosphere that meets the needs of the scene; infrared night vision control gives the device the ability to produce clear images in low-light or even no-light environments, greatly expanding the device's use time and spatial range.

[0003] At present, most RGB control and infrared night vision control are implemented relatively independently. In terms of RGB control, it is common to adjust the color parameters of the device based on preset color modes or through simple user input instructions. The color adjustment algorithm is often relatively fixed, and it is difficult to make real-time and flexible adaptive adjustments according to complex and changeable actual scenes. For example, in photography and video scenes, when the ambient light changes rapidly, RGB control cannot match the optimal color settings in a timely and accurate manner, resulting in color distortion or poor effect of the captured image.

[0004] For infrared night vision control, the existing technology usually automatically starts infrared fill light and switches the imaging mode when it detects that the ambient light intensity is lower than a certain threshold. However, this startup and switching mechanism lacks in-depth analysis of the specific situation of the scene, and is prone to over-fill light or under-fill light. For example, in security monitoring scenarios, when there are complex objects blocking or reflecting in the monitoring area, the infrared night vision control cannot intelligently adjust the emission intensity and angle of the infrared light, resulting in large spots of light or blurred imaging in some areas of the monitoring screen. Therefore, a method to improve the synergy between RGB control and infrared night vision control is needed. Summary of the invention

[0005] The invention provides an integrated system of RGB control and infrared night vision control, the main purpose of which is to improve the synergy between the RGB control and the infrared night vision control.

[0006] To achieve the above-mentioned purpose, the present invention provides an integrated system of RGB control and infrared night vision control, comprising: a scene priority determination module, a device resource selection module, a response time measurement module, a synergy effect evaluation module and an integrated control execution module;

[0007] The scene priority determination module is used to obtain the RGB device and the infrared night vision application scene, evaluate the scene criticality corresponding to the infrared night vision application scene, and query the control task type corresponding to the infrared night vision application scene, and set the control priority corresponding to the infrared night vision application scene in combination with the scene criticality and the control task type;

[0008] The device resource selection module is used to schedule device resources that can be used to control the RGB device and infrared night vision control, and collect the operation load data corresponding to the device resources, calculate the resource regulation energy efficiency coefficient corresponding to the device resources according to the operation load data, and select the scene control resources suitable for the infrared night vision application scenario from the device resources according to the resource regulation energy efficiency coefficient;

[0009] The response time calculation module is used to collect the device operating condition factor corresponding to the scene control resource, and calculate the response lag time of the RGB device in the scene control resource according to the device operating condition factor;

[0010] The synergy effect evaluation module is used to analyze the resource characteristic attributes corresponding to the scene control resources, determine the synergy coupling resources in the control resources according to the resource characteristic attributes, and combine the synergy coupling resources in combination with the response lag time to obtain the target synergy resources;

[0011] The integrated control execution module is used to set the integrated control scheme corresponding to the target collaborative resources in combination with the infrared night vision application scenario and the control priority, and according to the integrated control scheme, use the target collaborative resources to execute the infrared night vision integrated operation of the RGB device to obtain a control result.

[0012] Optionally, the evaluating the scene criticality corresponding to the infrared night vision application scene includes:

[0013] Dispatching historical event data of the infrared night vision application scenario, performing data cleaning on the historical event data, and obtaining target event data;

[0014] Extracting features from the target event data to obtain event features;

[0015] Analyzing the feature importance corresponding to the event feature, and performing standard processing on the feature importance to obtain a standard importance;

[0016] Based on the standard importance, calculating the importance score corresponding to the infrared night vision application scenario;

[0017] Based on the importance score, the scene criticality corresponding to the infrared night vision application scenario is evaluated.

[0018] Optionally, the step of setting the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type includes:

[0019] Extracting a task identifier corresponding to the control task type, and calculating an identifier information entropy corresponding to the task identifier;

[0020] Analyzing the representation identifier in the task identifier based on the identifier information entropy;

[0021] Calculating the identification similarity between the characterizing identifications, and classifying the control task types based on the identification similarity to obtain classified task types;

[0022] The time sensitivity corresponding to the characterization identifier is analyzed, and based on the time sensitivity and the scene criticality, the control priority corresponding to the infrared night vision application scene is set.

[0023] Optionally, the analyzing the time sensitivity corresponding to the characterization identifier includes:

[0024] Identify the identification timestamp in the representation identification, and assign the identification time point corresponding to the representation identification based on the identification timestamp;

[0025] Based on the identified time points, calculating the average time points corresponding to the characterizing identifiers;

[0026] Combining the identified time point and the average time point, the time sensitivity corresponding to the characterization mark is calculated by the following formula:

[0027]

[0028] Among them, A represents the time sensitivity corresponding to the characterization mark, t a+1 Indicates the identification time point corresponding to the a+1th identification in the representation identification, t a Indicates the identification time point corresponding to the ath identification in the representation identification, represents the average time point, a represents the sequence number corresponding to the characterization identifier, and n represents the number corresponding to the characterization identifier;

[0029] Based on the time sensitivity, the time sensitivity corresponding to the characterization identifier is analyzed.

[0030] Optionally, calculating the resource regulation energy efficiency coefficient corresponding to the device resource according to the operating load data includes:

[0031] Performing filtering on the operating load data to obtain target load data;

[0032] Querying the load function attribute corresponding to the target load data, and extracting the key load factor in the target load based on the load function attribute;

[0033] Allocating the control weight of the key load factor in the equipment resources, and quantifying the key load factor to obtain the key factor parameter;

[0034] Combining the control weight and the key factor parameter, the resource control energy efficiency coefficient corresponding to the device resource is calculated by the following formula:

[0035]

[0036] Among them, B represents the resource control energy efficiency coefficient corresponding to the equipment resources, and D b represents the key factor parameter of the bth key load factor in the equipment resources, β b represents the control weight of the bth critical load factor in the equipment resources, b represents the sequence number corresponding to the critical load factor, and m represents the number corresponding to the critical load factor.

[0037] Optionally, calculating the response lag time of the RGB device in the scene control resource according to the device operating condition factor includes:

[0038] Collecting the real-time data of the factors of the equipment operating condition factors in the scene control resources, and normalizing the real-time data of the factors to obtain normalized real-time data of the factors;

[0039] Extracting the factor statistical features corresponding to the normalized factor real-time data, and calculating the feature difference between the factor statistical features;

[0040] Based on the feature difference, performing feature selection processing on the factor statistical features to obtain representative statistical features;

[0041] Based on the representative statistical features and the preset delay mapping rule, the response delay duration of the RGB device in the scene control resource is calculated.

[0042] Optionally, the calculating the feature difference between the factor statistical features includes:

[0043] Vectorizing the statistical characteristics of the factors to obtain statistical characteristic vectors;

[0044] Based on the statistical feature vector, the feature difference between the two is calculated by the following formula:

[0045]

[0046] Among them, E represents the characteristic difference between the statistical characteristics of the factors, and Fd+1 Represents the statistical feature vector corresponding to the d+1th feature in the factor statistical feature, F d It represents the statistical feature vector corresponding to the dth feature in the factor statistical feature, d represents the serial number corresponding to the factor statistical feature, and q represents the number corresponding to the factor statistical feature.

[0047] Optionally, determining the cooperative coupling resource in the control resource according to the resource characteristic attribute includes:

[0048] Parsing the attribute dimensions corresponding to the resource characteristic attributes, and performing hierarchical sorting processing on the resource characteristic attributes based on the attribute dimensions to obtain hierarchical characteristic attributes;

[0049] Mining attribute association rules between the hierarchical feature attributes, and drawing an attribute association network between the hierarchical feature attributes based on the attribute association rules;

[0050] Evaluating the node association strength corresponding to each network node in the attribute association network;

[0051] Based on the node association strength, cooperative coupling resources in the control resources are determined.

[0052] Optionally, mining the attribute association rules between the hierarchical feature attributes includes:

[0053] Performing text mining on the hierarchical feature attributes to obtain feature attribute text;

[0054] Performing text semantic analysis on the feature attribute text to obtain feature attribute semantics;

[0055] Performing semantic annotation on the feature attribute semantics to obtain annotated attribute semantics;

[0056] The semantic association rules between the semantics of the annotation attributes are mined, and based on the semantic association rules, the attribute association rules between the hierarchical feature attributes are determined.

[0057] An integrated method of RGB control and infrared night vision control, characterized in that the method comprises:

[0058] Obtain RGB devices and infrared night vision application scenarios, evaluate the scene criticality corresponding to the infrared night vision application scenario, query the control task type corresponding to the infrared night vision application scenario, and set the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type;

[0059] Scheduling device resources that can be used to control the RGB device and infrared night vision control, and collecting operation load data corresponding to the device resources, calculating the resource regulation energy efficiency coefficient corresponding to the device resources according to the operation load data, and selecting the scene control resources suitable for the infrared night vision application scenario from the device resources according to the resource regulation energy efficiency coefficient;

[0060] Collecting the device operating condition factor corresponding to the scene control resource, and calculating the response lag time of the RGB device in the scene control resource according to the device operating condition factor;

[0061] Analyze the resource characteristic attributes corresponding to the scene control resource, determine the collaborative coupling resources in the control resource according to the resource characteristic attributes, and combine the collaborative coupling resources in combination with the response lag time to obtain the target collaborative resources;

[0062] In combination with the infrared night vision application scenario and the control priority, an integrated control scheme corresponding to the target collaborative resource is set. According to the integrated control scheme, the target collaborative resource is used to perform the infrared night vision integrated operation of the RGB device to obtain a control result.

[0063] By evaluating the scene criticality corresponding to the infrared night vision application scenario, the present invention can understand the importance of the infrared application scenario through the scene criticality, and provide an important basis for the subsequent setting of the control priority corresponding to the infrared night vision application scenario; optionally, the present invention calculates the resource regulation energy efficiency coefficient corresponding to the device resources according to the operating load data, and can accurately quantify the energy efficiency performance of the device resources under different working conditions, laying an important basis for the subsequent screening of scene control resources suitable for the infrared night vision application scenario from the device resources; the present invention calculates the response lag time of the RGB device in the scene control resource according to the device working condition factor, and can understand the real-time response efficiency of the RGB device in the scenario through the response lag time, so as to timely discover the device The invention can solve the performance bottleneck of the equipment, and provide strong support for optimizing the configuration of equipment resources and improving the overall operation smoothness of the system; the invention determines the collaborative coupling resources in the control resources according to the characteristic attributes of the resources, and can identify resources with inherent correlation and collaborative potential, greatly improving the efficiency of resource utilization; the invention sets the integrated control scheme corresponding to the target collaborative resources by combining the infrared night vision application scenario and the control priority, and fully considers the importance of different scene requirements and control, which can effectively improve the pertinence and rationality of the integrated control scheme, and according to the integrated control scheme, the target collaborative resources are used to perform the infrared night vision integrated operation of the RGB device, thereby ensuring the efficient and accurate execution of the infrared night vision integrated operation, and improving the overall performance of the RGB device in the infrared night vision scene. Therefore, the synergy of RGB control and infrared night vision control is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A functional module diagram of an integrated system of RGB control and infrared night vision control provided by one embodiment of the present invention;

[0065] Figure 2 A schematic flow chart of an integrated method of RGB control and infrared night vision control provided by an embodiment of the present invention;

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0069] In fact, the server device deployed by the integrated system of RGB control and infrared night vision control may be composed of one or more devices. The above-mentioned integrated system of RGB control and infrared night vision control can be implemented as: business instance, virtual machine, hardware device. For example, the integrated system of RGB control and infrared night vision control can be implemented as a business instance deployed on one or more devices in the cloud node. In simple terms, the integrated system of RGB control and infrared night vision control can be understood as a software deployed on the cloud node, which is used to provide integrated services of RGB control and infrared night vision control to each user terminal. Alternatively, the integrated system of RGB control and infrared night vision control can also be implemented as a virtual machine deployed on one or more devices in the cloud node. Application software for managing each user terminal is installed in the virtual machine. Alternatively, the integrated system of RGB control and infrared night vision control can also be implemented as a server composed of many hardware devices of the same or different types, and one or more hardware devices are set to provide integrated services of RGB control and infrared night vision control to each user terminal.

[0070] In terms of implementation, the integrated system of RGB control and infrared night vision control and the user end are adapted to each other. That is, the integrated system of RGB control and infrared night vision control is an application installed on the cloud service platform, and the user end is a client that establishes a communication connection with the application; or the integrated system of RGB control and infrared night vision control is implemented as a website, and the user end is implemented as a web page; or the integrated system of RGB control and infrared night vision control is implemented as a cloud service platform, and the user end is implemented as a small program in an instant messaging application.

[0071] Reference Figure 1 , which is a functional module diagram of an integrated system of RGB control and infrared night vision control provided by an embodiment of the present invention.

[0072] The integrated system 100 of RGB control and infrared night vision control of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as an integrated server, server cluster, etc. of RGB control and infrared night vision control), or it can be developed as a website. According to the functions implemented, the integrated system 100 of RGB control and infrared night vision control includes a scene priority determination module 101, a device resource selection module 102, a response time measurement module 103, a synergy effect evaluation module 104, and an integrated control execution module 105.

[0073] In an embodiment of the present invention, in the integrated tracking based on RGB control and infrared night vision control, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect multiple modules of another type and provide corresponding services to the multiple modules connected to it. In the integrated system of RGB control and infrared night vision control provided by the embodiment of the present invention, the scope of application of the integrated architecture of RGB control and infrared night vision control can be adjusted by adding modules and directly calling them without modifying the program code, so as to achieve cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the integrated system of RGB control and infrared night vision control. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0074] In the following, in conjunction with specific embodiments, each component and specific workflow of the integrated system of RGB control and infrared night vision control are described respectively.

[0075] The scene priority determination module 101 is used to obtain RGB devices and infrared night vision application scenarios, evaluate the scene criticality corresponding to the infrared night vision application scenario, and query the control task type corresponding to the infrared night vision application scenario, and set the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type.

[0076] By evaluating the scene criticality corresponding to the infrared night vision application scenario, the present invention can understand the importance of the infrared application scenario through the scene criticality, and provide an important basis for the subsequent setting of the control priority corresponding to the infrared night vision application scenario. It should be explained that the RGB device is a device with color image acquisition and processing functions, such as a common color camera; the infrared night vision application scenario refers to a scene that requires the use of infrared night vision technology for image acquisition and monitoring in an environment with insufficient light or no light, such as night security monitoring, outdoor night observation, etc.

[0077] In detail, the evaluation of the scene criticality corresponding to the infrared night vision application scene includes:

[0078] Dispatching historical event data of the infrared night vision application scenario, performing data cleaning on the historical event data, and obtaining target event data;

[0079] Extracting features from the target event data to obtain event features;

[0080] Analyzing the feature importance corresponding to the event feature, and performing standard processing on the feature importance to obtain a standard importance;

[0081] Based on the standard importance, calculating the importance score corresponding to the infrared night vision application scenario;

[0082] Based on the importance score, the scene criticality corresponding to the infrared night vision application scenario is evaluated.

[0083] It should be explained that the historical event data is a set of records of past events in the infrared night vision application scenario, the target event data is the available data after removing erroneous or incomplete data in the historical event data, the event features are the key information extracted from the target event data from dimensions such as the time, location, scope of impact, importance of persons involved or assets, the feature importance is the importance of the event feature when determining the criticality of the scene, the standard importance is the importance expressed under a unified measure of the feature importance, and the importance score is the importance score corresponding to the infrared night vision application scenario.

[0084] Furthermore, the historical event data of the infrared night vision application scenario can be scheduled through a database query statement, and the historical event data can be cleaned by writing a data cleaning algorithm to obtain target event data; the target event data can be feature extracted by a feature extraction algorithm to obtain event features; the feature importance corresponding to the event features can be analyzed by combining expert scoring with hierarchical analysis method, and the feature importance can be standardized by a maximum-minimum normalization method to obtain a standard importance; based on the standard importance, the importance score corresponding to the infrared night vision application scenario can be calculated by weighted summation, such as multiplying the standard importance corresponding to each event feature by the actual value of the feature in the current scene, and then adding all the product results to obtain the importance score; based on the numerical value of the importance score, the scene criticality corresponding to the infrared night vision application scenario is evaluated, and when the importance score is in a low segment, the scene criticality is low.

[0085] The present invention sets the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type. It can flexibly allocate resources according to actual needs and give priority to the execution of key scenes and important tasks, thereby significantly improving the response speed and accuracy of the monitoring system and reducing operating costs. It should be explained that the control task type is the operation task category determined for different monitoring needs corresponding to the infrared night vision application scenario, such as ensuring image clarity, adjusting infrared fill light intensity, realizing real-time data transmission or performing intelligent target recognition, etc., to clarify the core work goals that the device needs to achieve in this scenario. The control priority indicates the priority of each control task corresponding to the infrared night vision application scenario in resource allocation and execution order. Furthermore, the query of the control task type corresponding to the infrared night vision application scenario can be achieved by parsing the device configuration file.

[0086] In detail, the control priority corresponding to the infrared night vision application scenario is set in combination with the scene criticality and the control task type, including:

[0087] Extracting a task identifier corresponding to the control task type, and calculating an identifier information entropy corresponding to the task identifier;

[0088] Analyzing the representation identifier in the task identifier based on the identifier information entropy;

[0089] Calculating the identification similarity between the characterizing identifications, and classifying the control task types based on the identification similarity to obtain classified task types;

[0090] The time sensitivity corresponding to the characterization identifier is analyzed, and based on the time sensitivity and the scene criticality, the control priority corresponding to the infrared night vision application scene is set.

[0091] It should be explained that the task identifier is a concise and representative symbol or character string corresponding to the control task type, which is used to clearly distinguish different control tasks; the identifier information entropy indicates the uncertainty or richness of the information contained in the task identifier, reflecting the complexity of the task identifier in describing the task; the characterization identifier is a component of the task identifier that can highlight the key characteristics, properties or functions of the task; the identifier similarity indicates the degree of similarity between the characterization identifiers in terms of characteristics; the classified task type is the different categories obtained after clustering the control task type based on the identifier similarity, and tasks in the same category have high similarity in key characteristics; the time sensitivity indicates the strictness of the execution time of the task part corresponding to the characterization identifier, reflecting the urgency of completing the task within the specified time.

[0092] Furthermore, the task identifier corresponding to the control task type can be extracted by parsing the task configuration file or querying the task database, and the identification information entropy corresponding to the task identifier can be calculated by the Shannon entropy formula in information theory; based on the identification information entropy, a feature selection algorithm such as an information gain ratio algorithm is used to analyze the characterization identifier in the task identifier; the identification similarity between the characterization identifiers can be calculated by a cosine similarity algorithm or an edit distance algorithm, and based on the identification similarity, a clustering algorithm such as a K-Means clustering algorithm is used to classify the control task type to obtain a classified task type; based on the time sensitivity and the scene criticality, a priority calculation model such as a weighted sum model is constructed to comprehensively consider the weights of various factors to set the control priority corresponding to the infrared night vision application scenario.

[0093] Further, as an optional embodiment of the present invention, the analyzing the time sensitivity corresponding to the characterization identifier includes:

[0094] Identify the identification timestamp in the representation identification, and assign the identification time point corresponding to the representation identification based on the identification timestamp;

[0095] Based on the identified time points, calculating the average time points corresponding to the characterizing identifiers;

[0096] Combining the identified time point and the average time point, the time sensitivity corresponding to the characterization mark is calculated by the following formula:

[0097]

[0098] Among them, A represents the time sensitivity corresponding to the characterization mark, t a+1 Indicates the identification time point corresponding to the a+1th identification in the representation identification, t a Indicates the identification time point corresponding to the ath identification in the representation identification, represents the average time point, a represents the sequence number corresponding to the characterization identifier, and n represents the number corresponding to the characterization identifier;

[0099] Based on the time sensitivity, the time sensitivity corresponding to the characterization identifier is analyzed.

[0100] It should be explained that the identification timestamp is a character or digital mark in the representation identification used to record the time when a specific event or action occurs. It is accurate to a specific time scale and can be used to trace and analyze the sequence of events; the identification time point is the specific time position corresponding to a key event or task stage corresponding to the representation identification, representing a specific reference point of the representation identification in the time dimension; the average time point is the average time position corresponding to the representation identification obtained by statistical calculation of multiple related identification time points, reflecting a centralized trend of the representation identification in time; the time sensitivity indicates the sensitivity of the task or event corresponding to the representation identification to time changes, delays or time constraints, reflecting the influence of the time factor on the execution effect or goal achievement of the task related to the representation identification.

[0101] Furthermore, the identification timestamp in the characterization identifier can be identified through a regular expression matching algorithm, and based on the identification timestamp, the identification time point corresponding to the characterization identifier is allocated according to specific rules (such as converting the timestamp to a corresponding time format and then rounding the integer moment) according to the precise moment recorded by the timestamp; based on the identification time point, the average time point corresponding to the characterization identifier can be calculated through the arithmetic mean calculation method; based on the time sensitivity, a fuzzy comprehensive evaluation model is used to analyze the time sensitivity corresponding to the characterization identifier in combination with the time sensitivity value and other relevant time factors (such as time fluctuation range, strictness of time limit, etc.).

[0102] The device resource selection module 102 is used to schedule device resources that can be used to control the RGB device and infrared night vision control, and collect operating load data corresponding to the device resources, calculate the resource regulation energy efficiency coefficient corresponding to the device resources based on the operating load data, and screen out scene control resources suitable for the infrared night vision application scenario from the device resources based on the resource regulation energy efficiency coefficient.

[0103] The present invention calculates the resource regulation energy efficiency coefficient corresponding to the device resources based on the operating load data, and can accurately quantify the energy efficiency performance of the device resources under different working conditions, which lays an important basis for subsequently screening out scene control resources suitable for the infrared night vision application scenario from the device resources. It should be explained that the device resources are a general term for hardware facilities and software systems that can be used to control the RGB devices and infrared night vision controls, which provide necessary support for the operation and control of related equipment; the operating load data are real-time records of various working parameters corresponding to the device resources during the working process, which are used to comprehensively reflect the working status of the device resources; the resource regulation energy efficiency coefficient represents a quantitative indicator of the effectiveness of resource regulation corresponding to the device resources under different working conditions, which can intuitively show the resource utilization efficiency. Furthermore, the scheduling of device resources for controlling the RGB devices and infrared night vision controls can be achieved through xx.

[0104] In detail, the calculating, according to the operating load data, the resource regulation energy efficiency coefficient corresponding to the device resource includes:

[0105] Performing filtering on the operating load data to obtain target load data;

[0106] Querying the load function attribute corresponding to the target load data, and extracting the key load factor in the target load based on the load function attribute;

[0107] Allocating the control weight of the key load factor in the equipment resources, and quantifying the key load factor to obtain the key factor parameter;

[0108] Combining the control weight and the key factor parameter, the resource control energy efficiency coefficient corresponding to the device resource is calculated by the following formula:

[0109]

[0110] Among them, B represents the resource control energy efficiency coefficient corresponding to the equipment resources, and D b represents the key factor parameter of the bth key load factor in the equipment resources, β b represents the control weight of the bth critical load factor in the equipment resources, b represents the sequence number corresponding to the critical load factor, and m represents the number corresponding to the critical load factor.

[0111] It should be explained that the target load data is a data subset extracted from the operating load data after screening for specific analysis purposes or equipment resource types; the load function attributes are attribute descriptions corresponding to the target load data that reflect the working characteristics and performance of equipment resources in different functional dimensions; the key load factor is the core element in the target load that has a decisive influence on the performance, status and regulation effect of equipment resources; the regulation weight is the numerical weight assigned to the key load factor in the equipment resource based on its importance to the overall regulation of the equipment resource; the key factor parameter is the numerical parameter corresponding to the key load factor that has been quantified and is used to accurately measure and analyze the characteristics and status of the key load factor.

[0112] Optionally, the operating load data can be filtered by a program module based on a sliding average algorithm or a Kalman filter algorithm to obtain target load data; the load function attributes corresponding to the target load data can be queried through an associated database query engine and a pre-set attribute mapping table, and based on the load function attributes, the key load factors in the target load can be extracted by principal component analysis combined with expert experience judgment; the control weights of the key load factors in the equipment resources can be allocated by a hierarchical analysis method or a fuzzy comprehensive evaluation method, and the key load factors can be quantified by a normalization algorithm or a specific field quantitative model to obtain key factor parameters.

[0113] According to the resource regulation energy efficiency coefficient, the present invention selects scene control resources suitable for the infrared night vision application scenario from the device resources, thereby being able to accurately match the performance requirements of the scene for the device resources, while improving the operating stability and reliability of the infrared night vision system, minimizing resource waste, and effectively improving the operating efficiency and cost-effectiveness of the overall system. It should be explained that the scene control resources are a collection of various hardware and software resources in the device resources that are suitable for the infrared night vision application scenario and can meet its specific requirements for performance, power consumption, environmental adaptability, etc. in image acquisition, processing, transmission, and display. Furthermore, according to the resource regulation energy efficiency coefficient, scene control resources suitable for the infrared night vision application scenario are selected from the device resources, such as GPU devices, low-light cameras, and matching network communication modules that have high computing power and low power consumption to meet the needs of real-time image data processing, and have good adaptability to weak light environments and stable network transmission capabilities to ensure clear image transmission.

[0114] The response time calculation module 103 is used to collect the device operating condition factor corresponding to the scene control resource, and calculate the response lag time of the RGB device in the scene control resource according to the device operating condition factor.

[0115] The present invention calculates the response lag time of the RGB device in the scene control resource based on the device operating condition factor. The real-time response efficiency of the RGB device in the scene can be understood through the response lag time, and the device performance bottleneck can be discovered in time, so as to provide strong support for optimizing device resource configuration and improving the overall operation smoothness of the system. It should be explained that the device operating condition factor is various parameters corresponding to the scene control resource that reflect the device operating status and working conditions, such as load or network conditions and other factors; the response lag time indicates the time delay experienced by the RGB device from receiving an instruction to starting to execute a response action in the scene control resource, reflecting the response speed of the device to instructions in a specific scene. Furthermore, the collection of the device operating condition factor corresponding to the scene control resource can be achieved through various sensors deployed at key parts of the device, combined with adapted sensor drivers and efficient data acquisition software, and an intelligent algorithm for real-time verification of the collected data.

[0116] In detail, the calculating, according to the device operating condition factor, the response lag time of the RGB device in the scene control resource includes:

[0117] Collecting the real-time data of the factors of the equipment operating condition factors in the scene control resources, and normalizing the real-time data of the factors to obtain normalized real-time data of the factors;

[0118] Extracting the factor statistical features corresponding to the normalized factor real-time data, and calculating the feature difference between the factor statistical features;

[0119] Based on the feature difference, performing feature selection processing on the factor statistical features to obtain representative statistical features;

[0120] Based on the representative statistical features and the preset delay mapping rule, the response delay duration of the RGB device in the scene control resource is calculated.

[0121] It should be explained that the factor real-time data is the real-time actual value of the equipment operating condition factor in the scene control resource, the normalized factor real-time data is the data after the factor real-time data is normalized and mapped to a specific interval to eliminate the dimension effect, the factor statistical characteristics are statistical indicators such as mean, standard deviation, maximum value, etc. corresponding to the normalized factor real-time data for characterizing data characteristics, the feature difference degree indicates the degree of difference between the factor statistical characteristics measured by a specific measurement method (such as Euclidean distance, etc.), the representative statistical characteristics are screened based on the feature difference degree, retaining the characteristics that can highlight key information and remove redundancy, and the preset delay mapping rules are pre-set based on historical data, experience and domain knowledge, and are used to establish the criterion for the corresponding relationship between the representative statistical characteristics and the response lag time of the RGB device.

[0122] Furthermore, the real-time data of the factors of the equipment operating conditions in the scene control resources can be collected through the built-in sensors of the equipment, the monitoring software interface and the network data acquisition module, and the real-time data of the factors can be normalized by the normalization function to obtain the normalized real-time data of the factors, and the normalization function is compiled by the programming language; the statistical features of the factors corresponding to the normalized real-time data of the factors can be extracted by the data analysis library function (such as the relevant functions in the pandas and numpy libraries of Python); based on the feature difference, the statistical features of the factors are subjected to feature selection processing to obtain representative statistical features, such as using a set difference threshold to filter out features with a difference higher than the threshold, or using a recursive feature elimination algorithm to retain key features; based on the representative statistical features and the preset delay mapping rules, the response lag time of the RGB device in the scene control resources is calculated, such as matching the representative statistical features with the conditions in the rules, and calculating the time according to the formula or value method specified by the rules. If the rules stipulate that when the mean CPU usage is greater than 0.8 and the standard deviation of the memory occupancy is less than 0.1, the response lag time is the basic time of 30 milliseconds plus the maximum value of the network delay multiplied by a coefficient of 0.5, then it is calculated accordingly.

[0123] Further, as an optional embodiment of the present invention, the calculating the feature difference between the factor statistical features includes:

[0124] Vectorizing the statistical characteristics of the factors to obtain statistical characteristic vectors;

[0125] Based on the statistical feature vector, the feature difference between the two is calculated by the following formula:

[0126]

[0127] Among them, E represents the characteristic difference between the statistical characteristics of the factors, and Fd+1 Represents the statistical feature vector corresponding to the d+1th feature in the factor statistical feature, F d It represents the statistical feature vector corresponding to the dth feature in the factor statistical feature, d represents the serial number corresponding to the factor statistical feature, and q represents the number corresponding to the factor statistical feature.

[0128] It should be explained that the statistical feature vector is a vector composed of the statistical features of the factors arranged in a specific order. Furthermore, the statistical features of the factors can be vectorized by using the statistical features of each factor (such as mean, variance, maximum value, etc.) as the components of each dimension of the vector in a predetermined order to obtain the statistical feature vector.

[0129] The synergy effect evaluation module 104 is used to analyze the resource characteristic attributes corresponding to the scene control resources, determine the synergistic coupling resources in the control resources according to the resource characteristic attributes, and combine the synergistic coupling resources in combination with the response lag time to obtain the target synergistic resources.

[0130] The present invention determines the collaborative coupling resources in the control resources based on the resource characteristic attributes, and can identify resources with inherent correlation and collaborative potential, thereby greatly improving resource utilization efficiency. It should be explained that the resource characteristic attributes corresponding to the scenario control resources cover various characteristics of the resources, such as key information such as resource performance parameters, adjustable range, response speed, stability, etc. The collaborative coupling resources are a collection of resources in the control resources that are interrelated and influence each other in terms of function, performance or other key aspects, and can improve overall efficiency through collaborative work. Furthermore, the analysis of the resource characteristic attributes corresponding to the scenario control resources can be achieved by collecting the technical specifications and operation log data of the resources, and using data analysis algorithms and a combination of domain expert experience evaluation.

[0131] In detail, determining the cooperative coupling resource in the control resource according to the resource characteristic attribute includes:

[0132] Parsing the attribute dimensions corresponding to the resource characteristic attributes, and performing hierarchical sorting processing on the resource characteristic attributes based on the attribute dimensions to obtain hierarchical characteristic attributes;

[0133] Mining attribute association rules between the hierarchical feature attributes, and drawing an attribute association network between the hierarchical feature attributes based on the attribute association rules;

[0134] Evaluating the node association strength corresponding to each network node in the attribute association network;

[0135] Based on the node association strength, cooperative coupling resources in the control resources are determined.

[0136] It should be explained that the attribute dimension is the classification basis corresponding to the resource characteristic attributes, the hierarchical characteristic attributes are the characteristics obtained after hierarchical sorting and classification of the resource characteristic attributes based on the attribute dimension, the attribute association rules are the internal connection rules between the hierarchical characteristic attributes, the attribute association network is a relationship structure graph between the hierarchical characteristic attributes based on the attribute association rules, and the node association strength is the degree of connection between each network node in the attribute association network and other nodes.

[0137] Furthermore, the attribute dimensions corresponding to the resource characteristic attributes can be analyzed by combining domain knowledge with data analysis methods. Based on the attribute dimensions, the resource characteristic attributes can be hierarchically sorted to obtain hierarchical characteristic attributes. For example, according to attribute dimensions such as technological advancement, cost structure, and time span, resource characteristic attributes can be divided into characteristic attributes of different levels such as core technology, basic technology, direct cost, indirect cost, short-term, medium-term, and long-term. Based on the attribute association rules, the attribute association network between the hierarchical characteristic attributes can be drawn through graph modeling tools. The association strength calculation model can be used to evaluate each network node corresponding to the attribute association network. The corresponding node association strength, such as using a comprehensive calculation model constructed based on the weight of the node connection edge, the connection path length and the node's own importance index, to calculate the association strength between the technology research and development node and the marketing node in the resource collaborative attribute association network; based on the node association strength, by setting a threshold screening and combining with business scenario verification to determine the collaborative coupling resources in the control resources, such as setting the node association strength threshold to 0.6, screening out resource pairs with a strength higher than this value, and then verifying the synergy effect of technical resources and supply chain resources in the new product development business scenario to see whether they can shorten the R&D cycle and reduce costs, so as to finally determine whether they are collaborative coupling resources.

[0138] In detail, mining the attribute association rules between the hierarchical feature attributes includes:

[0139] Performing text mining on the hierarchical feature attributes to obtain feature attribute text;

[0140] Performing text semantic analysis on the feature attribute text to obtain feature attribute semantics;

[0141] Performing semantic annotation on the feature attribute semantics to obtain annotated attribute semantics;

[0142] The semantic association rules between the semantics of the annotation attributes are mined, and based on the semantic association rules, the attribute association rules between the hierarchical feature attributes are determined.

[0143] It should be explained that the feature attribute text is a specific description of the hierarchical feature attribute in text form after text mining processing, the feature attribute semantics is the intrinsic meaning and concept extracted from the feature attribute text through semantic analysis, the annotation attribute semantics is an expression with identification meaning given to the feature attribute semantics through a specific semantic annotation method, and the semantic association rule is a regular connection between the annotation attribute semantics based on co-occurrence relationships, semantic distances, etc.

[0144] Furthermore, the hierarchical feature attributes can be subjected to text mining through a natural language processing toolkit (such as NLTK, SpaCy) to obtain feature attribute text; the feature attribute text can be subjected to text semantic analysis through a semantic analysis model based on deep learning (such as fine-tuning of pre-trained models such as BERT and GPT) to obtain feature attribute semantics; the feature attribute semantics can be semantically annotated through professional semantic annotation tools (such as SemanticScholar, DBpediaSpotlight) in combination with custom annotation rules to obtain annotated attribute semantics; the semantic association rules between the annotated attribute semantics can be screened and mined through association rule mining algorithms (such as Apriori, FP-growth) in combination with domain knowledge, and the attribute association rules between the hierarchical feature attributes can be determined based on the semantic association rules. For example, in e-commerce product data, the purchase preference association rules derived from the semantic association rules between the hierarchical feature attributes such as "fashion style" and "favorite by young audiences" can be mined.

[0145] The present invention combines the collaborative coupling resources in combination with the response lag time, thereby effectively optimizing resource allocation, reducing efficiency loss caused by response delays, and improving the fluency and collaborative efficiency of the overall business process. It should be explained that the target collaborative resources are the resources obtained after screening, deploying and optimizing the collaborative coupling resources in combination with the response lag time. Furthermore, the collaborative coupling resources are combined in combination with the response lag time to obtain target collaborative resources. For example, in an integrated system of RGB control and infrared night vision control, image sensor resources with fast response speed are combined with an RGB dimming chip that can quickly adjust parameters and an infrared fill light driver module that can switch night vision mode in time, thereby greatly shortening the response time of the system when the light changes, thereby ensuring stable and efficient imaging and night vision functions.

[0146] The integrated control execution module 105 is used to set the integrated control scheme corresponding to the target collaborative resources in combination with the infrared night vision application scenario and the control priority, and according to the integrated control scheme, use the target collaborative resources to execute the infrared night vision integrated operation of the RGB device to obtain a control result.

[0147] The present invention combines the infrared night vision application scenario and the control priority to set the integrated control scheme corresponding to the target collaborative resources, fully considering the requirements of different scenarios and the importance of control, which can effectively improve the pertinence and rationality of the integrated control scheme, and according to the integrated control scheme, the target collaborative resources are used to perform the infrared night vision integrated operation of the RGB device, thereby ensuring the efficient and accurate execution of the infrared night vision integrated operation and improving the overall performance of the RGB device in the infrared night vision scenario. It should be explained that the integrated control scheme is a specific plan for the deployment and use of target collaborative resources to realize the infrared night vision integrated operation of RGB devices. Furthermore, the steps for setting the integrated control scheme corresponding to the target collaborative resources are as follows:

[0148] Assuming that the current infrared night vision application scenario is security monitoring, and the RGB device has a high priority for controlling the clarity of the monitoring images in key areas, at this time, the high-resolution infrared night vision camera resources are taken as the core, and its power supply stability and data transmission bandwidth are prioritized. The color parameters of the RGB device are adjusted to optimize the color tone and contrast of the key areas in the infrared night vision mode to highlight the monitoring target. At the same time, the calling frequency of the image enhancement algorithm resources is increased, and the collected images are processed in real time to enhance the picture details. For the fill light resources, an intelligent control strategy is adopted to automatically adjust the brightness of the infrared fill light according to the changes in the ambient light to ensure that the key areas always have sufficient and non-glaring light, and a quick response mechanism is set. When abnormal dynamics are detected in the key areas, the working performance of all related resources is immediately improved to ensure that clear and accurate monitoring images are obtained.

[0149] Assume that the infrared night vision application scenario is field exploration recording, and the RGB device has a high priority for portability and low power consumption control. Preferentially use small-sized, low-power infrared night vision sensors as one of the target collaborative resources, reduce the display brightness and color saturation of the RGB device to reduce energy consumption, but at the same time ensure that the image can clearly record the key information during the exploration process, optimize the management of data storage resources, and adopt efficient data compression algorithms to reduce storage space occupancy while ensuring acceptable image quality. For power supply resources, use energy-saving mode to prioritize the operation of key functions, such as automatically reducing the operating frequency of the device when there is no operation for a long time. And reasonably plan the sleep and wake-up mechanism of the device to ensure that when the infrared night vision function is needed, the device can be quickly awakened without affecting normal recording. In this way, the integrated control scheme corresponding to the target collaborative resource is generated.

[0150] By evaluating the scene criticality corresponding to the infrared night vision application scenario, the present invention can understand the importance of the infrared application scenario through the scene criticality, and provide an important basis for the subsequent setting of the control priority corresponding to the infrared night vision application scenario; optionally, the present invention calculates the resource regulation energy efficiency coefficient corresponding to the device resources according to the operating load data, and can accurately quantify the energy efficiency performance of the device resources under different working conditions, laying an important basis for the subsequent screening of scene control resources suitable for the infrared night vision application scenario from the device resources; the present invention calculates the response lag time of the RGB device in the scene control resource according to the device working condition factor, and can understand the real-time response efficiency of the RGB device in the scenario through the response lag time, so as to timely discover the device The invention can solve the performance bottleneck of the equipment, and provide strong support for optimizing the configuration of equipment resources and improving the overall operation smoothness of the system; the invention determines the collaborative coupling resources in the control resources according to the characteristic attributes of the resources, and can identify resources with inherent correlation and collaborative potential, greatly improving the efficiency of resource utilization; the invention sets the integrated control scheme corresponding to the target collaborative resources by combining the infrared night vision application scenario and the control priority, and fully considers the importance of different scene requirements and control, which can effectively improve the pertinence and rationality of the integrated control scheme, and according to the integrated control scheme, the target collaborative resources are used to perform the infrared night vision integrated operation of the RGB device, thereby ensuring the efficient and accurate execution of the infrared night vision integrated operation, and improving the overall performance of the RGB device in the infrared night vision scene. Therefore, the synergy of RGB control and infrared night vision control is improved.

[0151] like Figure 2 FIG. 1 is a flow chart of an integrated method for RGB control and infrared night vision control provided by an embodiment of the present invention. In this embodiment, the integrated method for RGB control and infrared night vision control includes:

[0152] S1. Obtain RGB equipment and infrared night vision application scenarios, evaluate the scene criticality corresponding to the infrared night vision application scenario, query the control task type corresponding to the infrared night vision application scenario, and set the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type;

[0153] S2, scheduling device resources that can be used to control the RGB device and infrared night vision control, and collecting operation load data corresponding to the device resources, calculating the resource regulation energy efficiency coefficient corresponding to the device resources according to the operation load data, and selecting the scene control resources suitable for the infrared night vision application scenario from the device resources according to the resource regulation energy efficiency coefficient;

[0154] S3, collecting the device operating condition factor corresponding to the scene control resource, and calculating the response lag time of the RGB device in the scene control resource according to the device operating condition factor;

[0155] S4, analyzing the resource characteristic attributes corresponding to the scene control resource, determining the collaborative coupling resources in the control resource according to the resource characteristic attributes, combining the collaborative coupling resources in combination with the response lag time, and obtaining the target collaborative resources;

[0156] S5. In combination with the infrared night vision application scenario and the control priority, an integrated control scheme corresponding to the target collaborative resource is set; according to the integrated control scheme, the target collaborative resource is used to perform the infrared night vision integrated operation of the RGB device to obtain a control result.

[0157] In the several embodiments provided by the present invention, it should be understood that the provided system and method can be implemented in other ways. For example, the system embodiment described above is only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0158] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An integrated system of RGB control and infrared night vision control, characterized in that: The integration of the system RGB control and infrared night vision control includes: a scene priority determination module, a device resource selection module, a response time measurement module, a synergy effect evaluation module and an integrated control execution module; The scene priority determination module is used to obtain the RGB device and the infrared night vision application scene, evaluate the scene criticality corresponding to the infrared night vision application scene, and query the control task type corresponding to the infrared night vision application scene, and set the control priority corresponding to the infrared night vision application scene in combination with the scene criticality and the control task type; The device resource selection module is used to schedule device resources that can be used to control the RGB device and infrared night vision control, and collect the operation load data corresponding to the device resources, calculate the resource regulation energy efficiency coefficient corresponding to the device resources according to the operation load data, and select the scene control resources suitable for the infrared night vision application scenario from the device resources according to the resource regulation energy efficiency coefficient; The response time calculation module is used to collect the device operating condition factor corresponding to the scene control resource, and calculate the response lag time of the RGB device in the scene control resource according to the device operating condition factor; The synergy effect evaluation module is used to analyze the resource characteristic attributes corresponding to the scene control resources, determine the synergy coupling resources in the control resources according to the resource characteristic attributes, and combine the synergy coupling resources in combination with the response lag time to obtain the target synergy resources; The integrated control execution module is used to set the integrated control scheme corresponding to the target collaborative resources in combination with the infrared night vision application scenario and the control priority, and according to the integrated control scheme, use the target collaborative resources to execute the infrared night vision integrated operation of the RGB device to obtain a control result.

2. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The evaluating of the scene criticality corresponding to the infrared night vision application scene includes: Dispatching historical event data of the infrared night vision application scenario, performing data cleaning on the historical event data, and obtaining target event data; Extracting features from the target event data to obtain event features; Analyzing the feature importance corresponding to the event feature, and performing standard processing on the feature importance to obtain a standard importance; Based on the standard importance, calculating the importance score corresponding to the infrared night vision application scenario; Based on the importance score, the scene criticality corresponding to the infrared night vision application scenario is evaluated.

3. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The step of setting the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type includes: Extracting a task identifier corresponding to the control task type, and calculating an identifier information entropy corresponding to the task identifier; Analyzing the representation identifier in the task identifier based on the identifier information entropy; Calculating the identification similarity between the characterizing identifications, and classifying the control task types based on the identification similarity to obtain classified task types; The time sensitivity corresponding to the characterization identifier is analyzed, and based on the time sensitivity and the scene criticality, the control priority corresponding to the infrared night vision application scene is set.

4. The integrated system of RGB control and infrared night vision control as claimed in claim 3, characterized in that: The analyzing the time sensitivity corresponding to the characterization identifier includes: Identify the identification timestamp in the representation identification, and assign the identification time point corresponding to the representation identification based on the identification timestamp; Based on the identified time points, calculating the average time points corresponding to the characterizing identifiers; Combining the identified time point and the average time point, the time sensitivity corresponding to the characterization mark is calculated by the following formula: Among them, A represents the time sensitivity corresponding to the characterization mark, t a+1 Indicates the identification time point corresponding to the a+1th identification in the representation identification, t a Indicates the identification time point corresponding to the ath identification in the representation identification, represents the average time point, a represents the sequence number corresponding to the characterization identifier, and n represents the number corresponding to the characterization identifier; Based on the time sensitivity, the time sensitivity corresponding to the characterization identifier is analyzed.

5. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The calculating, according to the operating load data, a resource control energy efficiency coefficient corresponding to the device resource comprises: Performing filtering on the operating load data to obtain target load data; Querying the load function attribute corresponding to the target load data, and extracting the key load factor in the target load based on the load function attribute; Allocating the control weight of the key load factor in the equipment resources, and quantifying the key load factor to obtain the key factor parameter; Combining the control weight and the key factor parameter, the resource control energy efficiency coefficient corresponding to the device resource is calculated by the following formula: Among them, B represents the resource control energy efficiency coefficient corresponding to the equipment resources, and D b represents the key factor parameter of the bth key load factor in the equipment resources, β b represents the control weight of the bth critical load factor in the equipment resources, b represents the sequence number corresponding to the critical load factor, and m represents the number corresponding to the critical load factor.

6. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The calculating, according to the device operating condition factor, the response lag time of the RGB device in the scene control resource comprises: Collecting the real-time data of the factors of the equipment operating condition factors in the scene control resources, and normalizing the real-time data of the factors to obtain normalized real-time data of the factors; Extracting the factor statistical features corresponding to the normalized factor real-time data, and calculating the feature difference between the factor statistical features; Based on the feature difference, performing feature selection processing on the factor statistical features to obtain representative statistical features; Based on the representative statistical features and the preset delay mapping rule, the response delay duration of the RGB device in the scene control resource is calculated.

7. The integrated system of RGB control and infrared night vision control as claimed in claim 6, characterized in that: The calculating the characteristic difference between the statistical characteristics of the factors includes: Vectorizing the statistical characteristics of the factors to obtain statistical characteristic vectors; Based on the statistical feature vector, the feature difference between the two is calculated by the following formula: Among them, E represents the characteristic difference between the statistical characteristics of the factors, and F d+1 Represents the statistical feature vector corresponding to the d+1th feature in the factor statistical feature, F d It represents the statistical feature vector corresponding to the dth feature in the factor statistical feature, d represents the serial number corresponding to the factor statistical feature, and q represents the number corresponding to the factor statistical feature.

8. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The determining, according to the resource characteristic attributes, the cooperative coupling resource in the control resource comprises: Parsing the attribute dimensions corresponding to the resource characteristic attributes, and performing hierarchical sorting processing on the resource characteristic attributes based on the attribute dimensions to obtain hierarchical characteristic attributes; Mining attribute association rules between the hierarchical feature attributes, and drawing an attribute association network between the hierarchical feature attributes based on the attribute association rules; Evaluating the node association strength corresponding to each network node in the attribute association network; Based on the node association strength, cooperative coupling resources in the control resources are determined.

9. The integrated system of RGB control and infrared night vision control as claimed in claim 1, characterized in that: The mining of attribute association rules between the hierarchical feature attributes includes: Performing text mining on the hierarchical feature attributes to obtain feature attribute text; Performing text semantic analysis on the feature attribute text to obtain feature attribute semantics; Performing semantic annotation on the feature attribute semantics to obtain annotated attribute semantics; The semantic association rules between the semantics of the annotation attributes are mined, and based on the semantic association rules, the attribute association rules between the hierarchical feature attributes are determined.

10. An integrated method of RGB control and infrared night vision control, characterized in that: The method comprises: Obtain RGB devices and infrared night vision application scenarios, evaluate the scene criticality corresponding to the infrared night vision application scenario, query the control task type corresponding to the infrared night vision application scenario, and set the control priority corresponding to the infrared night vision application scenario in combination with the scene criticality and the control task type; Scheduling device resources that can be used to control the RGB device and infrared night vision control, and collecting operation load data corresponding to the device resources, calculating the resource regulation energy efficiency coefficient corresponding to the device resources according to the operation load data, and selecting the scene control resources suitable for the infrared night vision application scenario from the device resources according to the resource regulation energy efficiency coefficient; Collecting the device operating condition factor corresponding to the scene control resource, and calculating the response lag time of the RGB device in the scene control resource according to the device operating condition factor; Analyze the resource characteristic attributes corresponding to the scene control resource, determine the collaborative coupling resources in the control resource according to the resource characteristic attributes, and combine the collaborative coupling resources in combination with the response lag time to obtain the target collaborative resources; In combination with the infrared night vision application scenario and the control priority, an integrated control scheme corresponding to the target collaborative resource is set. According to the integrated control scheme, the target collaborative resource is used to perform the infrared night vision integrated operation of the RGB device to obtain a control result.

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