Environment monitoring safety early warning system and method based on intelligent perception

Abstract

The invention provides an environment monitoring safety early warning system and method based on intelligent perception, and relates to the technical field of data intelligent identification, and the method comprises the steps: drawing a global sensing data topology network based on a plurality of sensors and a plurality of sensing data types; matching the target environment to activate the sensor; performing simulation sensing on a target environment, generating a sensing data type, and connecting a sensor with sensing data to draw a target environment sensing topology network; carrying out topological optimization on the target environment sensing topological network to generate a target environment sensing optimized topological network; executing multi-dimensional sensing of the target environment by utilizing the target environment sensing optimization topology network, and generating a multi-dimensional environment sensing data set of the target environment; and monitoring a target environment based on the multi-dimensional environment sensing data set, and triggering a safety early warning unit according to a monitoring result. According to the invention, the technical problem that the monitoring accuracy of the corresponding environment is reduced in the prior art can be solved, and the technical effect of environment adaptive monitoring is achieved.

Description

Technical Field

[0001] The present application relates to the field of data intelligent recognition technology, and in particular to an environmental monitoring safety early warning system and method based on intelligent perception. Background Art

[0002] The environmental monitoring safety early warning method based on intelligent perception refers to the use of intelligent perception technology to perceive and acquire substances and information in the environment, thereby achieving comprehensive monitoring and safety early warning of the environment.

[0003] At present, in the existing environmental monitoring process, most of them do not optimize the construction of topological networks based on sensors and sensor data types to monitor multiple environmental data, resulting in low recognition accuracy of the monitored environment, difficulty in calling the corresponding sensor network of the monitored environment for sensor monitoring, and unnecessary or poorly performing sensors for sensor monitoring, resulting in waste of monitoring resources and reduced monitoring accuracy. Therefore, a method is needed to solve the above problems.

[0004] In summary, there are technical problems in the prior art that most sensor networks for corresponding environmental monitoring are constructed with low accuracy, resulting in waste of sensor monitoring resources, reduced monitoring accuracy and efficiency, and further affecting the environmental safety warning effect. Summary of the invention

[0005] The purpose of this application is to provide an environmental monitoring safety early warning system and method based on intelligent perception, so as to solve the technical problems in the prior art that most sensor networks for corresponding environmental monitoring are constructed with low accuracy, resulting in waste of sensor monitoring resources, reduced monitoring accuracy and efficiency, and further affecting the effect of environmental safety early warning.

[0006] In view of the above problems, the present application provides an environmental monitoring safety early warning system and method based on intelligent perception.

[0007] In the first aspect, the present application provides an environment monitoring safety warning system based on intelligent perception, which is used to execute an environment monitoring safety warning method based on intelligent perception, wherein the system includes: a global sensor data topology network drawing module, which is used to draw a global sensor data topology network based on multiple sensors and multiple sensor data types, wherein the multiple sensors and the multiple sensor data types have a corresponding relationship; a sensor activation module, which is used to activate N sensors by matching the target environment through perception instructions and traversing the global sensor data topology network; a target environment sensor topology network drawing module, which is used to simulate sensing of the target environment through the N sensors, generating N sensor data types are generated, and the N sensors are connected with the N sensor data to draw a target environment sensor topology network, where N is a positive integer greater than or equal to 1; a target environment sensor optimization topology network generation module, which is used to perform topology optimization on the target environment sensor topology network to generate a target environment sensor optimization topology network; a multidimensional environment sensor data set generation module, which is used to perform multidimensional sensing of the target environment using the target environment sensor optimization topology network to generate a multidimensional environment sensor data set of the target environment; a monitoring result acquisition module, which is used to monitor the target environment based on the multidimensional environment sensor data set and trigger a safety warning unit according to the monitoring result.

[0008] In a second aspect, the present application also provides an environmental monitoring safety warning method based on intelligent perception, which is implemented by an environmental monitoring safety warning system based on intelligent perception, wherein the method includes: drawing a global sensor data topology network based on multiple sensors and multiple sensor data types, wherein there is a corresponding relationship between the multiple sensors and the multiple sensor data types; through perception instructions, matching the target environment to traverse the global sensor data topology network to activate N sensors; performing simulated sensing of the target environment through the N sensors to generate N sensor data types, connecting the N sensors with the N sensor data to draw the target environment sensor topology network, where N is a positive integer greater than or equal to 1; topologically optimizing the target environment sensor topology network to generate a target environment sensor optimization topology network; using the target environment sensor optimization topology network to perform multi-dimensional sensing of the target environment to generate a multi-dimensional environment sensor data set of the target environment; monitoring the target environment based on the multi-dimensional environment sensor data set, and triggering a safety warning unit according to the monitoring results.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] A global sensor data topology network is drawn through multiple sensors and multiple sensor data types, wherein the multiple sensors correspond to the multiple sensor data types; through perception instructions, the global sensor data topology network is traversed to activate N sensors by matching the target environment; the target environment is simulated sensed by the N sensors to generate N sensor data types, and the N sensors are connected with the N sensor data to draw the target environment sensor topology network, where N is a positive integer greater than or equal to 1; the target environment sensor topology network is topologically optimized to generate a target environment sensor optimization topology network; the target environment sensor optimization topology network is used to perform multi-dimensional sensing of the target environment to generate a multi-dimensional environment sensor data set of the target environment; the target environment is monitored based on the multi-dimensional environment sensor data set, and a safety warning unit is triggered according to the monitoring results. That is to say, by constructing and optimizing the global sensor topology network, the technical goal of adaptively performing intelligent data monitoring on all detected environments is finally achieved, the accuracy and efficiency of corresponding environment monitoring are improved, and the technical effect of improving environmental safety warning effect is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 This is a schematic diagram of the structure of the environmental monitoring safety early warning system based on intelligent perception in this application;

[0014] Figure 2 This is a flow chart of the environmental monitoring safety early warning method based on intelligent perception in this application.

[0015] Description of reference numerals:

[0016] A global sensor data topology network drawing module 11, a sensor activation module 12, a target environment sensor topology network drawing module 13, a target environment sensor optimization topology network generation module 14, a multi-dimensional environment sensor data set generation module 15, and a monitoring result acquisition module 16. DETAILED DESCRIPTION

[0017] This application provides an environmental monitoring safety early warning system and method based on intelligent perception, which solves the technical problem in the prior art that most sensor networks for corresponding environmental monitoring have low construction accuracy, resulting in waste of sensor monitoring resources, reduced monitoring accuracy and efficiency, and further affecting the environmental safety early warning effect. It achieves the technical goal of adaptively performing intelligent data monitoring on all detected environments, improves the accuracy and efficiency of environmental monitoring, and achieves the technical effect of improving the environmental safety early warning effect.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] Embodiment 1

[0020] Please see attached Figure 1 The present application provides an environment monitoring safety early warning system based on intelligent perception, wherein the system is applied to an environment monitoring safety early warning method based on intelligent perception, and the system specifically includes:

[0021] A global sensor data topology network drawing module 11, wherein the global sensor data topology network drawing module 11 is used to draw a global sensor data topology network based on a plurality of sensors and a plurality of sensor data types, wherein the plurality of sensors and the plurality of sensor data types have a corresponding relationship;

[0022] A sensor activation module 12, the sensor activation module 12 is used to activate N sensors by matching the target environment through the sensing instruction and traversing the global sensor data topology network;

[0023] A target environment sensor topology network drawing module 13, wherein the target environment sensor topology network drawing module 13 is used to simulate sensing of the target environment through the N sensors, generate N sensor data types, and connect the N sensors with the N sensor data to draw the target environment sensor topology network, where N is a positive integer greater than or equal to 1;

[0024] A target environment sensing optimization topology network generation module 14, wherein the target environment sensing optimization topology network generation module 14 is used to perform topology optimization on the target environment sensing topology network to generate a target environment sensing optimization topology network;

[0025] A multi-dimensional environment sensing data set generation module 15, wherein the multi-dimensional environment sensing data set generation module 15 is used to perform multi-dimensional sensing of the target environment using the target environment sensing optimization topology network to generate a multi-dimensional environment sensing data set of the target environment;

[0026] The monitoring result obtaining module 16 is used to monitor the target environment based on the multi-dimensional environmental sensing data set and trigger the safety warning unit according to the monitoring result.

[0027] Furthermore, the global sensor data topology network drawing module 11 in the system is also used for:

[0028] A historical sensor data set retrieval module, wherein the historical sensor data set retrieval module is used to retrieve historical sensor data sets based on a data cloud;

[0029] A sensor generation module, the sensor generation module is used to use the historical sensor data set as an index, traverse the sensor information library for matching, and generate the multiple sensors;

[0030] A sensing data type generating module, the sensing data type generating module is used to perform data classification based on the measurement ranges of the multiple sensors to generate the multiple sensing data types;

[0031] A sensor node generation module, the sensor node generation module is used to generate a plurality of sensor nodes and a plurality of sensor data type nodes based on the plurality of sensors and the plurality of sensor data types;

[0032] A data edge generation module, the data edge generation module is used to generate a plurality of data edges according to data flows between a plurality of sensors and a plurality of sensor data types;

[0033] A global sensor data topology network creation module is used to connect the multiple sensor nodes and the multiple sensor data type nodes through the multiple data edges to create the global sensor data topology network.

[0034] Further, the sensor activation module 12 in the system is also used for:

[0035] A panoramic image data acquisition module, the panoramic image data acquisition module is used to start the image acquisition device to acquire panoramic image data of the target environment;

[0036] A panoramic denoised image data generating module, the panoramic denoised image data generating module is used to perform image preprocessing on the panoramic image data to generate panoramic denoised image data;

[0037] An environment recognition result determination module, the environment recognition result determination module is used to extract multiple image features of the panoramic denoised image data, identify the target environment through a support vector machine, and determine an environment recognition result, the environment recognition result includes multiple types of data to be sensed;

[0038] A perception instruction generation module, wherein the perception instruction generation module is used to generate the perception instruction according to the environment recognition result.

[0039] Further, the sensor activation module 12 in the system is also used for:

[0040] a module for constructing a list of data types to be sensed, the module for constructing a list of data types to be sensed being used to execute the sensing instruction, define a target environment, and construct a list of data types to be sensed through the multiple data types to be sensed;

[0041] A sensor node set acquisition module, the sensor node set acquisition module is used to match the global sensor data topology network based on the list of types of data to be sensed, and identify and acquire a sensor node set;

[0042] A sensor node determination module, the sensor node determination module is used to extract the spatial layout information of the sensor node set, and determine N sensor nodes in combination with a plurality of layout location information;

[0043] A sensor activation module is used to activate the N sensors according to the N sensor nodes.

[0044] Furthermore, the target environment sensing optimization topology network generation module 14 in the system is also used for:

[0045] A topology network structure determination module, the topology network structure determination module is used to obtain the node connection relationship of the target environment sensor topology network, and determine the topology network structure according to the node connection relationship;

[0046] A key data set identification module, the key data set identification module is used to identify a key data set according to the topological network structure, the key data set includes a key node set and a key path set;

[0047] An optimization and adjustment module, the optimization and adjustment module is used to apply an ant colony algorithm to optimize and adjust based on the key node set and the key path set to generate M sensor nodes and their corresponding sensor data type nodes, where M is an integer not equal to N and greater than or equal to 1;

[0048] A target environment sensing optimization topology network construction module is used to construct the target environment sensing optimization topology network according to the M sensor nodes and their corresponding sensor data type nodes.

[0049] Furthermore, the target environment sensing optimization topology network generation module 14 in the system is also used for:

[0050] An initial path setting module, the initial path setting module is used to randomly distribute the first key node set to the starting node, and set the initial path based on the starting node;

[0051] A first pheromone quantity setting module, the first pheromone quantity setting module is used to set a first pheromone quantity for a first key path set based on historical data flow records, the first pheromone quantity including pheromone concentration and heuristic information;

[0052] A second pheromone quantity determination module, the second pheromone quantity determination module is used to start from the starting node and the initial path, select the next access node based on the pheromone concentration and the heuristic information, thereby performing iterative traversal, and after completing the access of all the first key node sets, update the first pheromone quantity of the first key path set to determine the second pheromone quantity;

[0053] A second key node set generation module, the second key node set generation module is used to update the first key path set by the second pheromone amount to generate a second key path set, and optimize and adjust the first key node set according to the second key path set to generate a second key node set;

[0054] A sensor data type node generation module is used to generate the M sensor nodes and their corresponding sensor data type nodes based on the second key path set and the second key node set.

[0055] Furthermore, the monitoring result obtaining module 16 in the system is also used for:

[0056] A safety threshold determination module, the safety threshold determination module is used to determine whether the multi-dimensional environmental sensor data set exceeds a safety threshold;

[0057] A trigger instruction generation module, wherein if yes, the target environment is deemed to have potential safety hazards, and a trigger instruction is generated and added to the monitoring result;

[0058] A security warning module is used to control the security warning unit to connect to a remote terminal to issue a security warning to the target environment according to the monitoring result and through the trigger instruction.

[0059] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The environmental monitoring safety early warning method based on intelligent perception and the specific examples are also applicable to the environmental monitoring safety early warning system based on intelligent perception in this embodiment. Through the detailed description of the environmental monitoring safety early warning method based on intelligent perception, those skilled in the art can clearly know the environmental monitoring safety early warning system based on intelligent perception in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0060] Embodiment 2

[0061] Based on the same inventive concept as the intelligent sensing-based environmental monitoring safety early warning system in the aforementioned embodiment, the present application also provides an intelligent sensing-based environmental monitoring safety early warning method, see the attached Figure 2 , the method comprising:

[0062] Step 1: drawing a global sensor data topology network based on a plurality of sensors and a plurality of sensor data types, wherein the plurality of sensors and the plurality of sensor data types have a corresponding relationship;

[0063] Specifically, according to the measurement range of each sensor, the sensors are classified into different sensor data types. Then, the corresponding relationship between multiple sensors and multiple sensor data types is obtained. According to multiple sensors and multiple sensor data types as network nodes, combined with the data flow status of sensors and multiple sensor data types, a global sensor data topology network is drawn.

[0064] Step 2: Through the sensing instruction, matching the target environment, traversing the global sensor data topology network to activate N sensors;

[0065] Specifically, the environment to be monitored is determined through the sensing instruction. The data type to be monitored of the target environment is obtained. The data type to be sensed is matched with the data type of each sensor node in the global sensor data topology network to identify and obtain the sensor node set. According to the sensor node set, N sensor nodes are determined to perform the monitoring task. Wherein, N is an integer greater than or equal to 1. The corresponding sensors are activated according to the N sensor nodes.

[0066] Step 3: Use the N sensors to simulate sensing of the target environment, generate N sensing data types, and connect the N sensors with the N sensing data to draw a target environment sensing topology network, where N is a positive integer greater than or equal to 1;

[0067] Specifically, N sensors are used to simulate the sensing of the target environment to generate N sensor data types, which represent different information collected by the N sensors from the target environment. After obtaining the N sensor data types, the N sensor data types are connected to the corresponding sensors. The connection is achieved through data edges, which represent the process of data flowing from sensors to data types. After the connection is completed, the target environment sensor topology network is drawn. The target environment sensor topology network uses sensors as nodes and data edges as connecting lines to display the data collected by sensors in the target environment. N is a positive integer greater than or equal to 1.

[0068] Step 4: topologically optimize the target environment sensor topological network to generate a target environment sensor optimized topological network;

[0069] Specifically, the node connection relationship of each sensor node and sensor data type node in the target environment sensor topology network is extracted to determine the overall structure of the target environment sensor topology network. The key data set is identified according to the topological network structure. Based on the key data set, the entire topology network is optimized and adjusted, the node position, connection relationship or adding / deleting nodes are adjusted, a more efficient and stable topology network structure is found, and the target environment sensor optimized topology network is generated.

[0070] Step 5: Utilize the target environment sensor optimization topology network to perform multi-dimensional sensing of the target environment, and generate a multi-dimensional environmental sensing data set of the target environment;

[0071] Specifically, according to the target environment sensor optimization topology network, the corresponding sensor nodes are configured, and environmental data is collected through each sensor node. After the sensor nodes are configured, multi-dimensional sensor data collection is performed, which means that multiple types of environmental data are collected at the same time, such as temperature, humidity, light, air pressure, etc. Each sensor node collects corresponding data according to its configuration to generate a multi-dimensional environmental sensing data set of the target environment.

[0072] Step six: Monitor the target environment based on the multi-dimensional environmental sensor data set, and trigger a safety warning unit according to the monitoring results.

[0073] Specifically, the safety threshold of each environmental parameter is pre-set. Each parameter value in the multi-dimensional environmental sensing data set is compared with the corresponding safety threshold. If there is a parameter value in the multi-dimensional environmental sensing data set that exceeds the corresponding safety threshold, it is identified that there is a safety hazard in the target environment, and a trigger instruction is generated to control the safety warning unit to take corresponding measures.

[0074] The environmental monitoring safety early warning method based on intelligent perception is applied to the environmental monitoring safety early warning system based on intelligent perception, which can achieve the technical goal of adaptively performing intelligent data monitoring on all detected environments, improve the accuracy and efficiency of environmental monitoring, and achieve the technical effect of improving the environmental safety early warning effect.

[0075] Furthermore, the present application also includes the following steps:

[0076] Retrieve historical sensor data sets based on the data cloud;

[0077] Using the historical sensor data set as an index, traversing the sensor information library for matching, and generating the plurality of sensors;

[0078] Performing data classification based on the measurement ranges of the multiple sensors to generate the multiple sensor data types;

[0079] Based on the multiple sensors and the multiple sensor data types, generate multiple sensor nodes and multiple sensor data type nodes;

[0080] Generate multiple data edges according to data flows between multiple sensors and multiple sensor data types;

[0081] The multiple sensor nodes and the multiple sensor data type nodes are connected through the multiple data edges to create the global sensor data topology network.

[0082] Specifically, historical sensor datasets are retrieved from the data cloud. The historical sensor datasets may come from a variety of different sensors, recording measurements at different times and locations.

[0083] Then, using the information from the historical sensor dataset, each database containing detailed information about various sensors is searched in turn. Through matching or comparison, all sensors that have appeared in the historical dataset can be identified and recorded.

[0084] Next, each sensor has a specific measurement range and type, for example, temperature, humidity, pressure, etc. Based on the measurement range of each sensor, the sensors are classified into different data types.

[0085] Next, multiple data edges are generated based on the data flow between multiple sensors and multiple sensor data types. A data edge represents the flow path of data in the network, and represents the relationship or data flow between the sensor and the corresponding data type. For example, a temperature sensor generates temperature data, and a data edge is generated from the temperature sensor node to the temperature data type node.

[0086] Then, in the process of building the data topology network, each sensor and each sensor data type is regarded as a node, representing a specific sensor or sensor data type. All sensor nodes and sensor data type nodes are connected using data edges to form a global sensor data topology network, thereby obtaining the relationship between sensors and data flow status.

[0087] By building a global sensor data topology network, we can obtain sensors, sensor data types and data flow status, and improve and optimize the performance and data flow of the sensor system.

[0088] Furthermore, the present application also includes the following steps:

[0089] Start the image acquisition device to obtain panoramic image data of the target environment;

[0090] Performing image preprocessing on the panoramic image data to generate panoramic denoised image data;

[0091] Extracting multiple image features of the panoramic denoised image data, identifying the target environment through a support vector machine, and determining an environment identification result, wherein the environment identification result includes multiple types of data to be sensed;

[0092] The perception instruction is generated according to the environment recognition result.

[0093] Specifically, the target environment is an environment to be monitored. An image acquisition device, such as a camera, a laser radar, an infrared sensor, etc., is started to capture a panoramic image of the target environment and obtain panoramic image data.

[0094] Then, the panoramic image data is subjected to image preprocessing to improve image quality and eliminate noise, thereby generating panoramic denoised image data. For example, the preprocessing may include operations such as color correction, contrast enhancement, and noise reduction to improve the accuracy of subsequent feature extraction and recognition.

[0095] Next, multiple image features of the panoramic denoised image data are extracted. Feature extraction is used to extract meaningful information from the image, such as edges, corners, textures, etc., for subsequent classification or recognition tasks. Support vector machine is a supervised learning algorithm, commonly used for classification and regression analysis. The target environment is classified or recognized by the support vector machine based on the extracted image features. For example, it is determined whether the target environment is indoors or outdoors, and the environment recognition result is determined. The environment recognition result contains multiple types of data to be sensed.

[0096] Next, the corresponding perception instructions are generated based on the environmental recognition results. The perception instructions may include adjusting sensor parameters such as focal length, exposure time, etc., triggering specific actions such as turning on or off lights, or sending alarms.

[0097] Through image processing, feature extraction, etc., environmental perception and recognition are performed to respond to the target environment and improve the recognition accuracy of the target environment.

[0098] Furthermore, the present application also includes the following steps:

[0099] Executing the sensing instruction, defining a target environment, and constructing a list of data types to be sensed by using the multiple data types to be sensed;

[0100] Matching the global sensor data topology network based on the list of types of data to be sensed, identifying and acquiring a sensor node set;

[0101] Extracting spatial layout information of the sensor node set, and determining N sensor nodes in combination with a plurality of layout location information;

[0102] The N sensors are activated according to the N sensor nodes.

[0103] Specifically, the environment to be monitored is determined through the sensing instructions. The target environment is defined, i.e., the physical characteristics of the target environment, the parameters to be monitored, and the expected data types. Based on the environmental recognition results, the data types to be sensed are obtained, such as temperature, humidity, light, etc. The data types to be sensed are organized into a list of data types to be sensed for subsequent matching in the global sensor data topology network.

[0104] Then, based on the list of types of data to be sensed, the data type of each sensor node in the global sensor data topology network is matched to identify and obtain the sensor node set.

[0105] Next, the sensor node set may contain multiple nodes that can monitor the same data type. In order to optimize the use and layout of sensors, the layout location information of the sensor node set is extracted. Such as the physical location and coverage of the sensor. Combined with the layout location information, N sensor nodes are determined to perform the monitoring task. Among them, N is an integer greater than or equal to 1.

[0106] Next, the corresponding sensors are activated according to the N sensor nodes. In this case, an activation command is sent to the sensor, or the state of the sensor is adjusted to start data collection. After activation, the target environment is monitored through the sensor node, and the data is sent back for analysis and processing.

[0107] Through the environmental identification results and the list of types of data to be sensed, suitable sensors are found and activated in the global sensor data topology network to achieve accurate perception and data collection of the target environment.

[0108] Furthermore, the present application also includes the following steps:

[0109] Acquire the node connection relationship of the target environment sensor topology network, and determine the topology network structure according to the node connection relationship;

[0110] Identify a key data set according to the topological network structure, wherein the key data set includes a key node set and a key path set;

[0111] Applying an ant colony algorithm to optimize and adjust based on the key node set and the key path set, generating M sensor nodes and their corresponding sensor data type nodes, where M is an integer not equal to N and greater than or equal to 1;

[0112] The target environment sensor optimization topology network is constructed according to the M sensor nodes and their corresponding sensor data type nodes.

[0113] Specifically, in the target environment sensor topology network, each sensor node and sensor data type node are connected through data edges to form a node connection relationship. The connection relationship between all nodes is extracted, and the node connection relationship determines the interaction and dependency between each node in the topology network. By analyzing the connection relationship, the overall structure of the target environment sensor topology network is determined.

[0114] Then, the key data set is identified based on the topological network structure. The key data set includes the key node set and the key path set. The key data set is the data in the topological network that is important for data transmission and environmental perception. According to the topological network structure, the nodes and paths that are crucial to maintaining the stability and efficiency of the topological network are identified to form the key node set and the key path set. The key node set includes sensors for important data. The key path set includes the main channels for data flow.

[0115] Next, the ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants and is suitable for solving optimization problems in complex networks. The ant colony algorithm is applied to optimize and adjust the entire topological network based on the key node set and the key path set, adjust the position and connection relationship of the nodes, or add / delete nodes, find a more efficient and stable topological network structure, ensure the transmission efficiency and accuracy of key data, and then generate M sensor nodes and their corresponding sensor data type nodes, where M is an integer not equal to N and greater than or equal to 1. Among them, since M is the number of sensor nodes after adjustment, M is either the number of nodes added is greater than N, or the number of nodes deleted is less than N. At the same time, the N sensor nodes are not completely deleted, so M is greater than or equal to 1.

[0116] Next, based on the M sensor nodes and their corresponding sensor data type nodes obtained after optimization by the ant colony algorithm, a sensor optimization topology network of the target environment is constructed, including the key nodes and paths in the original topology network, as well as more efficient node combinations and path selections found through the optimization algorithm.

[0117] According to the actual needs of the target environment and the structural characteristics of the topological network, the sensor nodes and sensor data type nodes are optimized and adjusted, so as to obtain the target environment sensor optimized topological network with higher data transmission efficiency, stronger stability and better environmental perception ability.

[0118] Furthermore, the present application also includes the following steps:

[0119] Randomly distribute to the starting node based on the first key node set, and set an initial path based on the starting node;

[0120] Setting a first pheromone amount for a first critical path set based on historical data flow records, wherein the first pheromone amount includes pheromone concentration and heuristic information;

[0121] Starting from the starting node and the initial path, selecting the next access node based on the pheromone concentration and the heuristic information, thereby performing iterative traversal, and after completing the access of all the first key node sets, updating the first pheromone amount of the first key path set, and determining the second pheromone amount;

[0122] Update the first key path set by using the second pheromone amount to generate a second key path set, and optimize and adjust the first key node set according to the second key path set to generate a second key node set;

[0123] The M sensor nodes and their corresponding sensor data type nodes are generated based on the second key path set and the second key node set.

[0124] Specifically, before starting the optimization process, a starting node is determined. The starting node can be randomly selected or determined based on a strategy, such as the importance of the node. The first set of key nodes is randomly distributed to the starting node, indicating that the key node is the focus of the optimization process. Based on the starting node, an initial path is set.

[0125] Then, the historical data flow record contains the flow of data in the topological network, including whether the path is frequently used and the transmission efficiency of the path is high. According to the historical data flow record, the initial pheromone amount, i.e., the first pheromone amount, is set for the first key path set. The first pheromone amount includes pheromone concentration and heuristic information. Pheromone concentration refers to the path length, and heuristic information refers to the node load.

[0126] Next, starting from the starting node and the initial path, the next node to be visited is selected based on the pheromone concentration and heuristic information on the current path. The iterative traversal selects the next node to be visited based on the pheromone concentration and heuristic information on the current path until all the first key node sets have been visited. After completing a round of traversal, the first pheromone amount on the first key path set is updated based on the movement trajectory and the access frequency. The updated pheromone amount is called the second pheromone amount. Among them, shorter paths or better solutions will be given more pheromones.

[0127] Next, the second pheromone amount is used to determine the key paths to be retained, deleted or added according to the adjusted second pheromone amount, and the first key path set is updated to generate a second key path set to optimize the flow efficiency and stability of data in the topological network.

[0128] Then, according to the second key path set and the second key node set, M sensor nodes and their corresponding sensor data type nodes are generated, which can better meet the perception needs of the target environment and improve the efficiency and stability of data transmission.

[0129] By optimizing and adjusting the target environment sensor topology network, a more efficient and stable node and path combination can be found.

[0130] Furthermore, the present application also includes the following steps:

[0131] Determining whether the multi-dimensional environmental sensing data set exceeds a safety threshold;

[0132] If yes, the target environment is considered to have potential safety hazards, and a trigger instruction is generated and added to the monitoring results;

[0133] According to the monitoring result, through the trigger instruction, the security warning unit is controlled to connect to the remote terminal to issue a security warning to the target environment.

[0134] Specifically, the safety thresholds of various environmental parameters are pre-set according to the characteristics and safety requirements of the target environment, and each parameter value in the multi-dimensional environmental sensing data set is compared with the corresponding safety threshold.

[0135] Then, if there are parameter values ​​in the multi-dimensional environmental sensing data set that exceed the corresponding safety threshold, then it is determined that there are safety hazards in the target environment, and the safety hazards in the target environment are identified, and a trigger instruction is generated and added to the monitoring result to trigger subsequent safety warning actions. If all parameter values ​​in the multi-dimensional environmental sensing data set are within the corresponding safety threshold, then the target environment is considered safe.

[0136] Then, according to the trigger instructions in the monitoring results, the security warning unit is controlled to take action. The security warning unit is a hardware device or software module used to perform security warning related operations. The remote terminal is a computer, mobile phone or other smart device used to receive and display security warning information. The remote terminal is connected to the remote terminal through the security warning unit, and the security warning information is sent to the remote terminal through the security warning unit. After receiving the security warning information, the remote terminal takes corresponding measures to deal with the security risks of the target environment.

[0137] By monitoring the security status of the target environment in real time and issuing timely security warnings when potential safety hazards are discovered, the security and stability of the target environment can be guaranteed.

[0138] In summary, the environmental monitoring safety early warning method based on intelligent perception provided by this application has the following technical effects:

[0139] A global sensor data topology network is drawn through multiple sensors and multiple sensor data types, wherein the multiple sensors correspond to the multiple sensor data types; through perception instructions, the global sensor data topology network is traversed to activate N sensors by matching the target environment; the target environment is simulated sensed by the N sensors to generate N sensor data types, and the N sensors are connected with the N sensor data to draw the target environment sensor topology network, where N is a positive integer greater than or equal to 1; the target environment sensor topology network is topologically optimized to generate a target environment sensor optimization topology network; the target environment sensor optimization topology network is used to perform multi-dimensional sensing of the target environment to generate a multi-dimensional environment sensor data set of the target environment; the target environment is monitored based on the multi-dimensional environment sensor data set, and a safety warning unit is triggered according to the monitoring results. That is to say, by constructing and optimizing the global sensor topology network, the technical goal of adaptively performing intelligent data monitoring on all detected environments is finally achieved, the accuracy and efficiency of environmental monitoring are improved, and the technical effect of improving the environmental safety warning effect is achieved.

[0140] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. The environmental monitoring safety early warning system based on intelligent perception is characterized by: The system comprises: A global sensor data topology network drawing module, the global sensor data topology network drawing module is used to draw a global sensor data topology network based on a plurality of sensors and a plurality of sensor data types, wherein the plurality of sensors and the plurality of sensor data types have a corresponding relationship; A sensor activation module, the sensor activation module is used to activate N sensors by matching the target environment through the sensing instruction and traversing the global sensor data topology network; A target environment sensor topology network drawing module, the target environment sensor topology network drawing module is used to simulate sensing of the target environment through the N sensors, generate N sensor data types, connect the N sensors with the N sensor data to draw the target environment sensor topology network, N is a positive integer greater than or equal to 1; A target environment sensing optimization topology network generation module, wherein the target environment sensing optimization topology network generation module is used to perform topology optimization on the target environment sensing topology network to generate a target environment sensing optimization topology network; A multi-dimensional environment sensing data set generation module, the multi-dimensional environment sensing data set generation module is used to perform multi-dimensional sensing of the target environment using the target environment sensing optimization topology network to generate a multi-dimensional environment sensing data set of the target environment; A monitoring result acquisition module is used to monitor the target environment based on the multi-dimensional environmental sensor data set and trigger a safety warning unit according to the monitoring results.

2. The intelligent sensing-based environmental monitoring safety early warning system according to claim 1, characterized in that: Draw a global sensor data topology network based on multiple sensors and multiple sensor data types, including: A historical sensor data set retrieval module, wherein the historical sensor data set retrieval module is used to retrieve historical sensor data sets based on a data cloud; A sensor generation module, the sensor generation module is used to use the historical sensor data set as an index, traverse the sensor information library for matching, and generate the multiple sensors; A sensing data type generating module, the sensing data type generating module is used to perform data classification based on the measurement ranges of the multiple sensors to generate the multiple sensing data types; A sensor node generation module, the sensor node generation module is used to generate a plurality of sensor nodes and a plurality of sensor data type nodes based on the plurality of sensors and the plurality of sensor data types; A data edge generation module, the data edge generation module is used to generate a plurality of data edges according to data flows between a plurality of sensors and a plurality of sensor data types; A global sensor data topology network creation module is used to connect the multiple sensor nodes and the multiple sensor data type nodes through the multiple data edges to create the global sensor data topology network.

3. The intelligent sensing-based environmental monitoring safety early warning system according to claim 1, characterized in that: The perception instruction includes: A panoramic image data acquisition module, the panoramic image data acquisition module is used to start the image acquisition device to acquire panoramic image data of the target environment; A panoramic denoised image data generating module, the panoramic denoised image data generating module is used to perform image preprocessing on the panoramic image data to generate panoramic denoised image data; An environment recognition result determination module, the environment recognition result determination module is used to extract multiple image features of the panoramic denoised image data, identify the target environment through a support vector machine, and determine an environment recognition result, the environment recognition result includes multiple types of data to be sensed; A perception instruction generation module, wherein the perception instruction generation module is used to generate the perception instruction according to the environment recognition result.

4. The intelligent sensing-based environmental monitoring safety early warning system according to claim 3, characterized in that: By sensing instructions, matching the target environment, traversing the global sensor data topology network to activate N sensors, including: a module for constructing a list of data types to be sensed, the module for constructing a list of data types to be sensed being used to execute the sensing instruction, define a target environment, and construct a list of data types to be sensed through the multiple data types to be sensed; A sensor node set acquisition module, the sensor node set acquisition module is used to match the global sensor data topology network based on the list of types of data to be sensed, and identify and acquire a sensor node set; A sensor node determination module, the sensor node determination module is used to extract the spatial layout information of the sensor node set, and determine N sensor nodes in combination with a plurality of layout location information; A sensor activation module is used to activate the N sensors according to the N sensor nodes.

5. The intelligent sensing-based environmental monitoring safety early warning system according to claim 1, characterized in that: Performing topological optimization on the target environment sensor topological network to generate a target environment sensor optimized topological network includes: A topology network structure determination module, the topology network structure determination module is used to obtain the node connection relationship of the target environment sensor topology network, and determine the topology network structure according to the node connection relationship; A key data set identification module, the key data set identification module is used to identify a key data set according to the topological network structure, the key data set includes a key node set and a key path set; An optimization and adjustment module, the optimization and adjustment module is used to apply an ant colony algorithm to optimize and adjust based on the key node set and the key path set to generate M sensor nodes and their corresponding sensor data type nodes, where M is an integer not equal to N and greater than or equal to 1; A target environment sensing optimization topology network construction module is used to construct the target environment sensing optimization topology network according to the M sensor nodes and their corresponding sensor data type nodes.

6. The intelligent sensing-based environmental monitoring safety early warning system according to claim 5, characterized in that: The ant colony algorithm is applied to optimize and adjust the key node set and the key path set to generate M sensor nodes and their corresponding sensor data type nodes, including: An initial path setting module, the initial path setting module is used to randomly distribute the first key node set to the starting node, and set the initial path based on the starting node; A first pheromone quantity setting module, the first pheromone quantity setting module is used to set a first pheromone quantity for a first key path set based on historical data flow records, the first pheromone quantity including pheromone concentration and heuristic information; A second pheromone quantity determination module, the second pheromone quantity determination module is used to start from the starting node and the initial path, select the next access node based on the pheromone concentration and the heuristic information, thereby performing iterative traversal, and after completing the access of all the first key node sets, update the first pheromone quantity of the first key path set to determine the second pheromone quantity; A second key node set generation module, the second key node set generation module is used to update the first key path set by the second pheromone amount to generate a second key path set, and optimize and adjust the first key node set according to the second key path set to generate a second key node set; A sensor data type node generation module is used to generate the M sensor nodes and their corresponding sensor data type nodes based on the second key path set and the second key node set.

7. The intelligent sensing-based environmental monitoring safety early warning system according to claim 1, characterized in that: Monitoring the target environment based on the multi-dimensional environmental sensor data set and triggering a safety warning unit according to the monitoring result include: A safety threshold determination module, the safety threshold determination module is used to determine whether the multi-dimensional environmental sensor data set exceeds a safety threshold; A trigger instruction generation module, wherein if yes, the target environment is deemed to have potential safety hazards, and a trigger instruction is generated and added to the monitoring result; A security warning module is used to control the security warning unit to connect to a remote terminal to issue a security warning to the target environment according to the monitoring result and through the trigger instruction.

8. The environmental monitoring safety early warning method based on intelligent perception is characterized by: The method comprises: Drawing a global sensor data topology network based on a plurality of sensors and a plurality of sensor data types, wherein the plurality of sensors and the plurality of sensor data types have a corresponding relationship; Through the sensing instruction, matching the target environment, traversing the global sensor data topology network to activate N sensors; Performing simulated sensing of the target environment through the N sensors to generate N types of sensing data, and connecting the N sensors with the N sensing data to draw a target environment sensing topology network, where N is a positive integer greater than or equal to 1; Performing topological optimization on the target environment sensor topological network to generate a target environment sensor optimized topological network; Utilizing the target environment sensing optimization topology network to perform multi-dimensional sensing of the target environment, and generating a multi-dimensional environmental sensing data set of the target environment; The target environment is monitored based on the multi-dimensional environmental sensing data set, and a safety early warning unit is triggered according to the monitoring result.

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