Task planning and loading method and system based on micro easy-to-use electromagnetic sensing device

Abstract

The application discloses a task planning and loading method and system based on a miniature easy-to-use electromagnetic sensing device, and the method comprises the following steps: summarizing various different task models by induction on a spectrum monitoring task, and uniformly encoding each task model to generate an instruction code table; generating a basic task template for different target tasks and target stages, and adjusting and generating a personalized task template set according to different task types, stages and spectrum monitoring task requirements; planning a task by using a heuristic task planning algorithm based on a key task priority rule according to the personalized template set, generating an optimal recommended task template for task planning, and generating a code type task loading instruction according to the instruction code table after the task planning is completed. Through the fast and simple task planning and loading technology, the application can efficiently and quickly generate a monitoring network planning scheme, and the loading can be completed in a short time, so that the electromagnetic environment monitoring task can be carried out anytime, anywhere and quickly.

Description

Technical Field

[0001] This invention relates to the field of radio monitoring technology, and in particular to a task planning and loading method and system based on a miniature, easy-to-use electromagnetic sensing device. Background Technology

[0002] Radio monitoring often requires a network of multiple monitoring stations to maximize coverage of the monitored area or target. Achieving maximum coverage with the fewest monitoring stations and optimizing station deployment are prerequisites for improving node deployment efficiency and extending network lifespan. However, due to the disconnect between current data and the actual geographical and spatial environment, significant adjustments are often required based on on-site surveys, a complex and inefficient process. While measurement estimation can obtain data from the actual environment, obtaining detailed data requires extensive measurements, is time-consuming and labor-intensive, and cannot cover the entire monitoring range, affecting the accuracy of the estimation results. Furthermore, non-professionals lack specialized monitoring knowledge, making it difficult to propose effective solutions for optimizing monitoring network planning.

[0003] Meanwhile, as equipment networks become increasingly complex, the control of individual internal units becomes more refined and flexible. Monitoring systems need to process a large number of remote control commands and inject data to ensure the normal execution of each task, thus increasing the occupancy rate of remote control channel resources. Designing and sending remote control commands one by one or arranging multiple commands into a command data chain in sequence is time-consuming, requires a lot of manpower and channel resources, and also brings certain risks to the normal execution of tasks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a task planning and loading method and system based on miniature and easy-to-use electromagnetic sensing devices. Through rapid and simple task planning and loading technology, a monitoring network planning scheme can be generated efficiently and quickly, and loading can be completed in a short time, enabling electromagnetic environment monitoring tasks to be carried out anytime, anywhere, and quickly.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device includes:

[0007] Modular modeling of tasks is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table.

[0008] The scenario is associated with the task. For different target tasks and target stages, a dynamically configurable basic task template is generated. The basic task template is selected according to different task types and task stages to generate basic spectrum monitoring task requirements and make adjustments to add or remove different task modules. A personalized task template set is generated according to different operating styles.

[0009] The spectrum monitoring task is intelligently optimized by using a heuristic task planning algorithm based on priority rules for key tasks to plan tasks according to a set of personalized templates, and generating the best recommended task template.

[0010] Tasks are loaded quickly. Task planning is performed based on recommended task templates. After task planning is completed, code-based task loading instructions are generated according to the instruction code table and then sent out through available communication links.

[0011] The process of generating dynamically configurable basic task templates specifically includes: modeling monitoring tasks using the Activiti engine; the monitoring task modeling process includes process template configuration and monitoring task configuration; wherein,

[0012] The process template configuration process includes:

[0013] a) Process template drawing: The EST BPM3 online process editor is used as the tool for drawing process templates. The editor contains four types of process elements: connection, event, task and gateway; the task elements are divided into monitoring data writing, form approval, processing verification and document entry according to function; the process template is drawn using the editor.

[0014] b) Deployment of process templates: After the process template is drawn, a process description file is generated and parsed. The process description file is then configured with the IDs of each element node and the generated XML description file is converted into an XML description file conforming to the BPMN 2.0 standard. Access, configuration, and usage permissions are set for the process template under different roles, and the process template is deployed to the PVM process virtual machine through the Activiti5 process engine.

[0015] Furthermore, the process of parsing the process description file is as follows: After the process template is drawn, the Dom4j tool is used to parse the process description file of the process template and read it into a Document object. The doc.getElement() method is used to obtain all the tag elements in the description file, and the tag name and tag object of each tag element are encapsulated into a Map in key-value pairs.<String,Object> In the map collection of type, each tag name is further filtered out using StartEvent, Task, Gateway, and EndEvent as keys. The tag value, tag ID, and tag type in the tag object are obtained. The tag value, tag ID, and tag type obtained from the tag object are then encapsulated into a JSON string and asynchronously sent to the browser.

[0016] Furthermore, the process of deploying the process template to the PVM process virtual machine through the Activiti5 process engine is as follows: The Activiti5 process engine's resource service component, RepositoryService, calls the createDeployment() method to obtain a builder object. Executing the builder.deploy() method successfully deploys the current process description file to the PVM process virtual machine and generates a process definition DEFID. Simultaneously, the XML description file and PNG image file of the flowchart are stored in the resource table. Finally, the parameter information of each element tag in the process description file is stored in the node status table, using the process definition DEFID as a foreign key to distinguish the relationship between each task node data and the process template.

[0017] Specifically, the monitoring task configuration process includes the following sub-processes:

[0018] Task planning and deployment: After successful deployment of the process template, select the solution and the deployed process template, formulate the monitoring task plan, and design the task plan configuration parameters, including the task's planned time, task volume, task urgency, task type, and task source; use the process definition DEFID to obtain all process nodes in the node status table, save the information of these soon-to-be-started process nodes to the activity table, and assign the task management personnel and set the task start and end times to the ASSIGNEE field reserved for each process node data; finally, use the process startup method of the runtime service component RuntimeService to publish the task process with the process definition DEFID as the parameter, generate a process instance object ProceesInstance and return the process instance ID; at the same time as the process starts, populate the management personnel information assigned to the first process node of the current task into the ASSIGNEE_ field of the ACT_RU_TASK table that has the same process instance ID record;

[0019] Task Node Processing and Progress Monitoring: When administrators query pending tasks, they use the TaskService component to retrieve the tasks to be processed. When processing a task, they parse the node ID to obtain the associated monitoring form and fill in the data. After the form is submitted and the task processing is completed, the TaskService component is used to end the processing of the current node. Then, the completed status is updated to the node status table. Finally, the ASSIGNEE task handler for the next process node in the activity table is queried and updated to the ASSIGNEE_ field of the new task in the ACT_RU_TASK table.

[0020] When monitoring task progress, the project manager needs to provide feedback on the flowchart and indicate the location of the currently executing task nodes. First, a `ProcessDiagramCanvas` class needs to be defined, declaring various drawing components from the `java.awt` package. After reading the XML process description file, the class constructor is used to initialize the drawing of task nodes, events, gateways, and sequence flows based on the coordinates and side lengths of each process element, defining their size, shape, and position. Simultaneously, the process instance ID is used to query the node status table, encapsulating the node status color in a Map collection with `KEY` as the nodeId and `VAULE` as the node status color. During the drawing process, nodes in different execution states will be displayed according to the corresponding colors encapsulated in the Map.

[0021] Furthermore, the specific steps for generating code-based task loading instructions based on the instruction code table include:

[0022] Instruction sequence loading: The method of injecting instruction sequences is adopted. By uniformly encapsulating and processing indirect instructions, short frame instructions are transformed into long frame injection data.

[0023] Data compression and encapsulation of instruction sequences: A multi-instruction data compression and encapsulation method is used to extract key and necessary information from remote control instructions and simplify the instruction encapsulation information content;

[0024] Design and implementation of dedicated instruction tasks: A series of operations are combined into a dedicated instruction. When the dedicated instruction is loaded, the computer selects a pre-set task flow according to the instruction and executes each instruction in sequence.

[0025] Dynamic adjustment of dedicated task instructions: The relative execution time of each independent instruction within a dedicated task instruction is adjusted according to the different application scenarios or conditions of the dedicated task instruction.

[0026] Furthermore, the heuristic task planning algorithm based on the priority rule of key tasks is used for task planning. Specifically, this includes: constructing a monitoring network monitoring efficiency evaluation index system, using the positions of all n monitoring stations in the radio monitoring network as particle coordinates, evaluating the deployment efficiency of the monitoring stations through the particle swarm optimization algorithm, obtaining the collaborative monitoring repetition rate, and adjusting the monitoring areas or monitoring targets of different monitoring stations in the radio monitoring network according to the evaluation results of the deployment efficiency of the monitoring stations.

[0027] Furthermore, the unified coding process for each task model specifically includes: first, constructing a task model based on the task type, which includes rapid continuous scanning of a specified frequency band, collaborative scanning of a specified frequency band, timed monitoring and listening, and radiation source localization; then, uniformly coding the task parameters in the task model.

[0028] A task planning and loading system based on a miniature, easy-to-use electromagnetic sensing device, implemented using the aforementioned task planning and loading method, comprises a task model construction module, a task scenario association module, a solution recommendation module, and an instruction loading module; wherein...

[0029] The task model building module is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table.

[0030] The task scenario association module is used to generate dynamically configurable basic task templates for different target tasks and target stages. It selects basic task templates according to different task types and task stages, generates basic spectrum monitoring task requirements, and adds or removes different task modules. It also generates personalized task template sets according to different operating styles.

[0031] The solution recommendation module is used to perform task planning based on a heuristic task planning algorithm with priority rules for key tasks, based on a set of personalized templates, and generate the best recommended task template.

[0032] The instruction loading module is used to plan tasks based on recommended task templates. After the task planning is completed, it generates code-based task loading instructions based on the instruction code table and sends the code-based task loading instructions through an available communication link.

[0033] The beneficial effects of this invention are:

[0034] 1. By adopting the monitoring task modeling and dictionary based on the Activiti engine and the FreeMarker template engine, the problems of process template configuration and monitoring task configuration are solved. Operators can automatically generate various monitoring tasks by inputting simple key monitoring parameters.

[0035] 2. The deployment algorithm is optimized by using intelligent optimization technology for monitoring tasks, the deployment efficiency is evaluated, the repetition rate of repeated collaborative monitoring is obtained, and the monitoring areas or monitoring targets of different monitoring stations are rationally planned to achieve full coverage of monitoring tasks as much as possible.

[0036] 3. The use of code-based task fast loading technology solves the problems of low device task loading rate and large number of uploaded instructions within a limited time. The task data is compressed and packaged into instruction sequences and combined with a remote loading method of dedicated task instructions. Through analysis of task requirements and characteristics, the remote loading data content is simplified and the data injection efficiency is improved. At the same time, the loaded task instructions can be controlled and modified through external modification instructions, which enhances the flexibility of instruction tasks.

[0037] 4. This invention reduces the professional skill requirements for task planning by using preset scenario templates, thus improving the efficiency of task planning. Simultaneously, loading task instructions via code messages significantly reduces the amount of task instruction data, effectively lowering task loading time and improving the reliability and security of task loading. This technology enhances the comprehensive, efficient, and precise sensing capabilities of electromagnetic environment monitoring equipment, while reducing the professional skill requirements for operators. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method of the present invention.

[0039] Figure 2 This is a functional diagram of the system software of the present invention.

[0040] Figure 3 This is a software workflow diagram of the present invention.

[0041] Figure 4 This is a flowchart of the description file parsing process of the present invention.

[0042] Figure 5 This is a flowchart of the process template configuration of the present invention.

[0043] Figure 6 This is a flowchart of the monitoring task configuration process of the present invention.

[0044] Figure 7 This is a flowchart of the fuzzy factor optimization based on PSO of the present invention.

[0045] Figure 8 This is a breakdown diagram of the monitoring network monitoring efficiency evaluation index system of the present invention.

[0046] Figure 9 This is a flowchart of the task loading process of the present invention. Detailed Implementation

[0047] To provide a clearer understanding of the technical features, objectives, and beneficial effects of this invention, the following detailed description of the technical solution is provided. Obviously, the described embodiments are only a portion of the embodiments of this invention, not all of them, and should not be construed as limiting the scope of implementation of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of this invention.

[0048] Example 1:

[0049] In this embodiment, as Figure 1 As shown, a task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device includes:

[0050] Modular modeling of tasks is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table.

[0051] The scenario is associated with the task. For different target tasks and target stages, a dynamically configurable basic task template is generated. The basic task template is selected according to different task types and task stages to generate basic spectrum monitoring task requirements and make adjustments to add or remove different task modules. A personalized task template set is generated according to different operating styles.

[0052] The spectrum monitoring task is intelligently optimized by using a heuristic task planning algorithm based on priority rules for key tasks to plan tasks according to a set of personalized templates, and generating the best recommended task template.

[0053] Tasks are loaded quickly. Task planning is performed based on recommended task templates. After task planning is completed, code-based task loading instructions are generated according to the instruction code table and then sent out through available communication links.

[0054] In this embodiment, the detailed process of the method is as follows:

[0055] I. Modular Modeling of Spectrum Monitoring Tasks

[0056] The possible spectrum monitoring tasks are systematically reviewed, various task models are summarized, and each task model is uniformly coded to generate an instruction code table.

[0057] II. Relationship between Target Tasks and Spectrum Monitoring Tasks

[0058] For different target tasks and stages, the required spectrum monitoring tasks are identified, and dynamically configurable task templates are generated. Operators can select task templates according to different task types and stages, automatically generate basic spectrum monitoring task requirements, and add, remove, or adjust different task modules based on these requirements. Personalized templates can also be generated and saved according to different operating styles.

[0059] III. Intelligent Optimization of Spectrum Monitoring Tasks

[0060] Based on the task template set finally generated by the operator, a heuristic task planning algorithm based on the priority rule of key tasks is used for task planning, and the best intelligent recommendation result is provided for the operator to choose. Following the task template method, the operator starts task planning from accepting the task and selecting the task template. Without making a lot of adjustments, the time required to generate the code-based loading instruction will not exceed 1.5 minutes.

[0061] IV. Fast loading of code-based tasks

[0062] After task planning is completed, code-based task loading instructions are automatically generated based on the task code table. These code-based instructions significantly compress the length of transmitted information, effectively improving task loading speed and enabling one-click loading, while also enhancing the stealth of the task loading process. Under conditions of a stable communication link, task loading time should not exceed 0.5 minutes.

[0063] In this embodiment, the process of generating a dynamically configurable basic task template specifically includes: using a monitoring task modeling and dictionary based on the Activiti engine and the FreeMarker template engine to solve the problems of process template configuration and monitoring task configuration.

[0064] The monitoring task modeling process consists of two main stages: process template configuration and monitoring task configuration. Process template configuration can be further divided into two steps: process template drawing and process template deployment. Monitoring task configuration can be further divided into task plan formulation and release, and task node handling and progress monitoring.

[0065] (1) Process template configuration stage

[0066] Workflow Template Drawing: The EST BPM3 online workflow editor is used as the tool for drawing workflow templates. The editor contains four main types of workflow elements: connections, events, tasks, and gateways. When drawing templates using the editor, task elements are mainly divided into monitoring data writing, form approval, processing verification, and document entry, etc., and can be combined in different ways with various gateways in different task management workflows.

[0067] To bind each task node element to the process-related monitoring forms, after the template is drawn, the Dom4j tool is used to parse the template's process description file and read it into a Document object. The doc.getElement() method is used to retrieve all tag elements in the description file, and the "tag name" and "tag object" of each tag element are encapsulated in a key-value format into a Map.<String,Object> In a map collection of types, each tag is further filtered using "StartEvent", "Task", "Gateway", and "EndEvent" as keys. The resulting elements (tag values, tag IDs, tag types, etc.) are then encapsulated into a JSON string and asynchronously sent to the browser. The parsing process of the process description file is as follows: Figure 4 As shown:

[0068] After generating the standard process description file, access, configuration, and usage permissions are set for the process template under different roles. Finally, the `createDeployment()` method is called through the resource service component `RepositoryService` of the Activiti5 process engine to obtain the `builder` object. Executing the `builder.deploy()` method successfully deploys the current process description file to the PVM process virtual machine and generates the process definition DEFID. Simultaneously, two data entries are generated in the `ACT_GE_BYTEARRAY` resource table:

[0069] 1) The XML description file of the flowchart is stored in compressed binary form in the BYTES_ field of the ACT_GE_BYTEARRAY table, generating the first data entry;

[0070] 2) The PNG image file of the flowchart is stored in compressed binary form in the BYTES_ field of the ACT_GE_BYTEARRAY table, generating the second data entry.

[0071] Meanwhile, to facilitate the retrieval of tag content from the process description file, the parameter information of each element tag is stored in the second-level BPM_NODE_SET node status table. The process definition DEFID is used as a foreign key to distinguish the relationship between each task node data and the process template. This separates process data from deployed template data, facilitating program querying and retrieval. In summary, the first-stage process template configuration is as follows: Figure 5 As shown.

[0072] (2) Monitoring task configuration phase

[0073] like Figure 6 As shown, the monitoring task configuration process includes task plan formulation and release, task node processing and progress monitoring.

[0074] Task plan creation and publication: After the process template is successfully deployed, select both the solution and the deployed process template. After confirming the creation of the monitoring plan, a record will be generated in the PDPMS_PROJSCHEMA plan table, where the primary key ID of the solution table and the primary key DEFID of the process definition table serve as foreign keys. Simultaneously, task plan parameters need to be configured, including detailed information such as the task's planned time, workload, urgency, type, and source.

[0075] Then, using the process definition DEFID, all process nodes are retrieved from the node status table. The information of these process nodes that are about to be started is saved in the PDPMS_PROJACTIVITIY activity table. The task management personnel are assigned to the ASSIGNEE field reserved for each process node data, and the start and end times of the task are set.

[0076] Finally, the process startup method of the runtime service component RuntimeService is used to publish the task process with the process definition DEFID as a parameter, generating a process instance object ProceesInstance and returning the process instance ID. Simultaneously with process startup, the administrator information assigned to the first process node of the current task is populated into the ASSIGNEE_ field of the ACT_RU_TASK table containing a record with the same process instance ID. The corresponding administrator can then log in to perform the monitoring task.

[0077] Task Node Processing and Progress Monitoring: When administrators query pending tasks, they use the `taskAssignee(assigneeID).list()` method of the `TaskService` component to retrieve the pending tasks. When processing a task, the associated monitoring form is retrieved by parsing the node ID, and relevant data is filled in. After the form is submitted and the task processing is completed, the processing of the current node is first ended using the `complete(task)` method of the `TaskService` component. Then, the "Completed" status is updated in the node status table. Finally, the `ASSIGNEE` task handler for the next process node in the activity table is queried, and the `ASSIGNEE_` field of the new task in the `ACT_RU_TASK` table is updated.

[0078] When monitoring task progress, project managers need to provide flowcharts and indicate the current task nodes.

[0079] First, a `ProcessDiagramCanvas` class needs to be defined, declaring various drawing components from the `java.awt` package, such as `Graphics2D` and `FontMetrics`. After reading the XML process description file, the class constructor is used to initialize the drawing of task nodes, events, gateways, and sequence flows based on the coordinates, side lengths, and other information of each process element, determining their size, shape, and position. Simultaneously, the process instance ID is used to query the node status table for the status of each node. The node status color is encapsulated in a Map collection, with `key` as `nodeId` and `VAULE` as the node's color. During the drawing process, nodes in different execution states will be displayed according to the corresponding colors encapsulated in the Map.

[0080] In this embodiment, the unified coding process for each task model specifically includes: first, constructing a task model based on the task type, which includes rapid continuous scanning of a specified frequency band, collaborative scanning of a specified frequency band, timed monitoring and listening, and radiation source localization.

[0081] (1) Rapid and continuous scanning of a specified frequency band

[0082] Upon receiving an instruction, the system needs to scan and perceive the wireless environment within the specified frequency band as quickly as possible. In this case, the monitoring network needs to mobilize multiple monitoring nodes to participate in the spectrum scanning task, with each node scanning a different frequency range. This type of task is suitable for monitoring short-lived signals, and collaborative monitoring is suitable for building the task model.

[0083] (2) Cooperative scanning of the specified frequency band

[0084] After receiving such an instruction, the system will only arrange some nodes or even a single node to perform spectrum monitoring. The allocation rules can be based on the location and battery level. This type of task is suitable for building a task model using collaborative monitoring or single-point monitoring.

[0085] (3) Regular monitoring and listening

[0086] This type of task can instruct monitoring nodes to monitor specific frequency points or bands in the two methods mentioned above according to specified cycles and time points. This type of task is suitable for building task models using single-point monitoring.

[0087] (4) Radiation source location

[0088] Based on the current power status and network connectivity of the nodes, the data processing center management terminal designates multiple monitoring nodes to form a direction finding and positioning network to achieve signal source localization. This type of task is suitable for using distributed monitoring to build the task model.

[0089] Then, the task parameters in the task model are uniformly encoded, and the specific encoding content is shown in Table 1 below.

[0090] Table 1 Task Parameter Encoding Table

[0091]

[0092] In this embodiment, generating code-based task loading instructions according to the instruction code table specifically includes: remote loading of task data through a combination of compressed and encapsulated instruction sequences and dedicated task instructions. By analyzing task requirements and characteristics, the remote loading data content is simplified. Simultaneously, external modification instructions allow for control and modification of the loaded task instructions. This invention employs a remote loading method combining compressed and encapsulated instruction sequences with dedicated task instructions. Through analysis of task requirements and characteristics, the remote loading data content is simplified, improving data injection efficiency. Furthermore, external modification instructions allow for control and modification of the loaded task instructions, enhancing the flexibility of the instruction task. The specific code-based task loading instruction generation process is as follows:

[0093] (1) Loading instruction sequence

[0094] By employing an instruction sequence injection method, short-frame instructions are transformed into long-frame injection data through unified encapsulation and processing of indirect instructions. This method can significantly reduce the use of minimum protection padding codes for remote control instruction transmission and improve resource utilization.

[0095] (2) Data compression and encapsulation instruction sequence

[0096] By employing a multi-instruction data compression and encapsulation method, key and necessary information in remote control instructions is extracted, simplifying the instruction encapsulation information content and improving instruction loading efficiency.

[0097] (3) Design and implementation of special instruction tasks

[0098] A series of operations can be combined into a single dedicated instruction. Once the instruction is loaded, the computer selects a pre-set task flow based on the instruction and executes each instruction sequentially.

[0099] This design approach not only reduces the time spent on instruction setup and processing but also significantly lowers the complexity of instruction sequences and improves instruction reliability. Such new instructions, synthesized from multiple fixed-process instruction sequences, are called dedicated task instructions.

[0100] (4) Dynamic adjustment of special task instructions

[0101] The relative execution time of each independent instruction within a dedicated task instruction is fixed. However, in certain special applications or under certain conditions, it is necessary to appropriately modify the relative execution time of one or a few instructions. Therefore, an adjustment instruction for the dedicated task instruction is designed.

[0102] In this embodiment, the task planning process using a heuristic task planning algorithm based on priority rules for key tasks specifically includes: constructing a monitoring network monitoring efficiency evaluation index system; using the positions of all n monitoring stations in the radio monitoring network as particle coordinates; evaluating the deployment efficiency of the monitoring stations using a particle swarm optimization algorithm; obtaining the collaborative monitoring repetition rate; and adjusting the monitoring areas or monitoring targets of different monitoring stations in the radio monitoring network based on the evaluation results of the deployment efficiency of the monitoring stations.

[0103] Particle swarm optimization (PSO) can be used to solve the problem of optimizing the deployment of monitoring stations in radio monitoring network planning. In PSO, each solution is abstracted as a particle, representing a solution in the solution space of the optimization problem. Each particle adjusts its position and velocity based on its past movement paths and the movement paths of other particles in the swarm. During the optimization process, PSO first sets an appropriate number of particles and randomly assigns a value to each particle. Then, it calculates the fitness value of each particle based on the objective function; this fitness value represents the particle's fitness level. Next, the particles continuously adjust their position and velocity based on their own or the swarm's historical best positions, searching for the optimal solution in the solution space.

[0104] The particle swarm optimization algorithm has the following advantages: it is easy to implement and does not require much parameter tuning; global information sharing makes it easier for particles to obtain the optimal value in the individual or in the population; and the algorithm increases its computation speed by using only the optimal information of the individual and the optimal information of the population.

[0105] like Figure 7As shown in the figure, this is a flowchart of the fuzzy factor optimization based on PSO. The steps of the particle swarm optimization algorithm are as follows:

[0106] 1) Initialize several particles.

[0107] 2) Calculate the fitness value for each particle.

[0108] 3) Update the optimal position based on the fitness value of each particle, and update the global optimal position of the entire population.

[0109] 4) Update the position and velocity of each particle.

[0110] 5) Enhance population diversity by applying mutation operators. The operation is as follows: generate a random value for each dimension of each particle and compare it with a predetermined mutation probability.

[0111] 6) If the number of iterations exceeds the maximum value, the optimization process ends; otherwise, it returns to step 2.

[0112] The process of the particle swarm optimization algorithm is as follows:

[0113] Each particle has a position vector The defined location, where i Let be the index number of the particle, and let its velocity be determined by the velocity vector. This means that each particle records its own optimal position. Therefore, the optimal position vector from the group is stored in In the vector.

[0114] During iteration time k In this context, the definition of a speed being updated from an old speed to a new speed is:

[0115]

[0116] in 1 is the inertia factor, and 2 are the acceleration constants.

[0117] Calculate the new position using the sum of the previous position and the new velocity:

[0118]

[0119] The algorithm parameters are set as follows during the monitoring task planning process:

[0120] Using the positions of all n stations as particle coordinates, i.e., particle coordinates are .

[0121] The evaluation of monitoring station deployment efficiency involves a comprehensive assessment of the deployment efficiency of multiple monitoring stations working together. It requires considering factors such as monitoring coverage, the number of sensors, and monitoring redundancy rates. The evaluation index system can be broken down into, for example, […]. Figure 8 As shown.

[0122] The definitions of each indicator in the indicator system are as follows:

[0123] 1) Collaborative monitoring coverage

[0124] The goal of optimizing monitoring station deployment is to achieve maximum monitoring coverage with the fewest monitoring stations. For regional monitoring, assuming the monitoring coverage of a single monitoring station is known, the corresponding monitoring area is... Since the monitoring areas of different monitoring stations may overlap, the collaborative monitoring area of ​​multiple monitoring stations is defined as the union of the monitorable areas of all monitoring stations. Therefore, the collaborative monitoring coverage H is determined by the following formula:

[0125]

[0126] In the formula: i = 1, 2, ..., k, k is the number of monitoring stations deployed, X is the monitoring task area, and Area() is the area of ​​the region.

[0127] For point target monitoring, assume the set of measurable targets for the i-th monitoring station. Common goals Since different monitoring stations may have overlapping targets, the set of targets monitored collaboratively by multiple monitoring stations is defined as the union of the sets of targets that all monitoring stations can monitor. The collaborative monitoring coverage H is then determined by the following formula:

[0128]

[0129] In the formula: i = 1, 2, ..., k, k is the number of monitoring stations deployed, W is the set of monitoring task targets, and Count() is the number of targets in the target set.

[0130] 2) Collaborative monitoring of repetition rate

[0131] To improve the utilization rate of monitoring stations, significant overlap between the monitoring areas or targets of different monitoring stations should be avoided as much as possible. In other words, the overlap rate of collaborative monitoring should be low. For regional monitoring, the overlapping area of ​​collaborative monitoring by multiple monitoring stations is the intersection of the monitorable areas of all monitoring stations. Therefore, the overlap rate of collaborative monitoring is defined as the ratio of the overlapping area to the total collaborative monitoring area, calculated by the following formula:

[0132]

[0133] For point target detection, the set of duplicate targets monitored collaboratively by multiple monitoring stations is the intersection of the sets of targets that can be monitored by all monitoring stations. Therefore, the collaborative monitoring duplication rate is defined as the ratio of the number of duplicate targets monitored collaboratively to the total number of targets monitored collaboratively.

[0134] .

[0135] In this embodiment, as Figure 9 As shown, when a node in a miniature electromagnetic environment monitoring device loads tasks, referencing the multi-level caching approach used in central processing units, routine task parameters are first cached in the instruction set. Task parameter reception and routine parameter reading occur simultaneously, or routine parameter reading precedes task parameter reception. Local routine received parameters are read first, followed by task parameter loading. When real-time task parameters are received, parsed, and compared with routine parameters, only the differing parameters are loaded. Since the probability of adjusting frequently used parameters is low, fewer parameters require reloading, thus improving the task parameter loading speed.

[0136] Example 2:

[0137] In this embodiment, based on the method provided in Embodiment 1, a task planning and loading system based on a miniature, easy-to-use electromagnetic sensing device is designed. The system includes a task model construction module, a task scenario association module, a solution recommendation module, and an instruction loading module; wherein,

[0138] The task model building module is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table.

[0139] The task scenario association module is used to generate dynamically configurable basic task templates for different target tasks and target stages. It selects basic task templates according to different task types and task stages, generates basic spectrum monitoring task requirements, and adds or removes different task modules. It also generates personalized task template sets according to different operating styles.

[0140] The solution recommendation module is used to perform task planning based on a heuristic task planning algorithm with priority rules for key tasks, based on a set of personalized templates, and generate the best recommended task template.

[0141] The instruction loading module is used to plan tasks based on recommended task templates. After the task planning is completed, it generates code-based task loading instructions based on the instruction code table and sends the code-based task loading instructions through an available communication link.

[0142] In this embodiment, the task planning and loading system based on miniature, easy-to-use electromagnetic sensing devices includes fast and simple task planning and loading functions. It is responsible for scientifically and rationally planning monitoring strategies, dividing tasks, and generating spectrum monitoring tasks for different devices based on factors such as the target task, monitoring target, monitoring device status, and monitoring method. These tasks are then loaded onto designated monitoring devices in a pre-loading or dynamic loading manner. The monitoring devices then start monitoring work according to the loaded tasks and the specified plan. Its system software functions include, for example, […]. Figure 2 The functions shown.

[0143] (1) Modular modeling of spectrum monitoring tasks

[0144] The possible spectrum monitoring tasks are systematically reviewed, various task models are summarized, and each task model is uniformly coded to generate an instruction code table.

[0145] (2) Correlation between target task and spectrum monitoring task

[0146] For different target tasks and stages, the required spectrum monitoring tasks are identified, and dynamically configurable task templates are generated. Operators can select task templates according to different task types and stages, automatically generate basic spectrum monitoring task requirements, and add, remove, or adjust different task modules based on these requirements. Personalized templates can also be generated and saved according to different operating styles.

[0147] (3) Intelligent optimization of spectrum monitoring tasks

[0148] Based on the task template set finally generated by the operator, a heuristic task planning algorithm based on the priority rule of key tasks is used for task planning, and the best intelligent recommendation result is provided for the operator to choose. Following the task template approach, the operator starts task planning from accepting the task and selecting the task template. Without needing to make a lot of adjustments, the time required to generate the code-based loading instruction will not exceed 1.5 minutes.

[0149] (4) Fast loading of code-based tasks

[0150] After task planning is completed, code-based task loading instructions are automatically generated based on the task code table. These code-based instructions significantly compress the length of transmitted information, effectively improving task loading speed and enabling one-click loading, while also enhancing the stealth of the task loading process. Under conditions of a stable communication link, task loading time should not exceed 0.5 minutes.

[0151] Quick and simple task planning and loading are the foundation for the entire system to start working. This is responsible for scientifically and rationally planning monitoring strategies based on factors such as target tasks, monitoring objectives, monitoring device status, and monitoring methods; dividing tasks; and generating spectrum monitoring tasks for different devices. These tasks are then loaded onto the designated monitoring devices either pre-loaded or dynamically. The monitoring devices then start monitoring work according to the specified plan based on the loaded tasks. The overall workflow is shown in the figure below.

[0152] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device, characterized in that, include: Modular modeling of tasks is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table. The scenario is associated with the task. For different target tasks and target stages, a dynamically configurable basic task template is generated. The basic task template is selected according to different task types and task stages to generate basic spectrum monitoring task requirements and make adjustments to add or remove different task modules. A personalized task template set is generated according to different operating styles. The spectrum monitoring task is intelligently optimized by using a heuristic task planning algorithm based on priority rules for key tasks to plan tasks according to a set of personalized templates, and generating the best recommended task template. Tasks are loaded quickly. Task planning is performed based on recommended task templates. After task planning is completed, code-based task loading instructions are generated based on the instruction code table and sent out through available communication links. The specific steps of generating code-based task loading instructions based on the instruction code table include: Instruction sequence loading: The method of injecting instruction sequences is adopted. By uniformly encapsulating and processing indirect instructions, short frame instructions are transformed into long frame injection data. Data compression and encapsulation of instruction sequences: A multi-instruction data compression and encapsulation method is used to extract key and necessary information from remote control instructions and simplify the instruction encapsulation information content; Design and implementation of dedicated instruction tasks: A series of operations are combined into a dedicated instruction. When the dedicated instruction is loaded, the computer selects a pre-set task flow according to the instruction and executes each instruction in sequence. Dynamic adjustment of dedicated task instructions: The relative execution time of each independent instruction within a dedicated task instruction is adjusted according to the different application scenarios or conditions of the dedicated task instruction.

2. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 1, characterized in that, The process of generating dynamically configurable basic task templates specifically includes: using monitoring task modeling based on the Activiti engine, the monitoring task modeling process includes process template configuration and monitoring task configuration; wherein, the process template configuration process includes: a) Process template drawing: The EST BPM3 online process editor is used as the tool for drawing process templates. The editor contains four types of process elements: connection, event, task and gateway; the task elements are divided into monitoring data writing, form approval, processing verification and document entry according to function; the process template is drawn using the editor. b) Deployment of process templates: After the process template is drawn, a process description file is generated and parsed. The process description file is then configured with the IDs of each element node and the generated XML description file is converted into an XML description file conforming to the BPMN 2.0 standard. Access, configuration, and usage permissions are set for the process template under different roles, and the process template is deployed to the PVM process virtual machine through the Activiti5 process engine.

3. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 2, characterized in that, The process of parsing the process description file is as follows: After the process template is drawn, the Dom4j tool is used to parse the process description file of the process template and read it into a Document object. All tag elements in the description file are obtained through the doc.getElement() method. The tag name and tag object of each tag element are encapsulated into a Map in key-value format.<String,Object> In the map collection of type, each tag name is further filtered out using StartEvent, Task, Gateway, and EndEvent as keys. The tag value, tag ID, and tag type in the tag object are obtained. The tag value, tag ID, and tag type obtained from the tag object are then encapsulated into a JSON string and asynchronously sent to the browser.

4. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 2, characterized in that, The specific process of deploying the process template to the PVM process virtual machine through the Activiti5 process engine is as follows: The Activiti5 process engine's resource service component, RepositoryService, calls the createDeployment() method to obtain a builder object. Executing the builder.deploy() method successfully deploys the current process description file to the PVM process virtual machine and generates a process definition DEFID. Simultaneously, the XML description file and PNG image file of the flowchart are stored in the resource table. Finally, the parameter information of each element tag in the process description file is stored in the node status table, using the process definition DEFID as a foreign key to distinguish the relationship between each task node data and the process template.

5. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 2, characterized in that, The monitoring task configuration process specifically includes the following sub-processes: Task planning and deployment: After the process template is successfully deployed, select the solution and the deployed process template, formulate the monitoring task plan, and design the task plan configuration parameters, including the task's planned time, task volume, task urgency, task type, and task source; Using the process definition DEFID, all process nodes are retrieved from the node status table. Information on these upcoming process nodes is then saved to the activity table. For each process node, the ASSIGNEE field is reserved to assign a task administrator and set the task start and end times. Finally, the runtime service component RuntimeService is used to handle the flow. The process startup method publishes the task process with the process definition DEFID as a parameter, generates a process instance object ProceesInstance and returns the process instance ID; at the same time as the process starts, the management personnel information assigned to the first process node of the current task is filled into the ASSIGNEE_ field of the ACT_RU_TASK table that has a record with the same process instance ID. Task Node Processing and Progress Monitoring: When administrators query pending tasks, they use the TaskService component to retrieve the tasks to be processed. When processing a task, they parse the node ID to obtain the associated monitoring form and fill in the data. After the form is submitted and the task processing is completed, the TaskService component is used to end the processing of the current node. Then, the completed status is updated to the node status table. Finally, the ASSIGNEE task handler for the next process node in the activity table is queried and updated to the ASSIGNEE_ field of the new task in the ACT_RU_TASK table. When monitoring task progress, the project manager needs to provide feedback on the flowchart and indicate the location of the currently executing task nodes. First, a `ProcessDiagramCanvas` class needs to be defined, declaring various drawing components from the `java.awt` package. After reading the XML process description file, the class constructor is used to initialize the drawing of task nodes, events, gateways, and sequence flows based on the coordinates and side lengths of each process element, defining their size, shape, and position. Simultaneously, the process instance ID is used to query the node status table, encapsulating the node status color in a Map collection with `KEY` as the nodeId and `VAULE` as the node status color. During the drawing process, nodes in different execution states will be displayed according to the corresponding colors encapsulated in the Map.

6. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 1, characterized in that, The task planning using a heuristic task planning algorithm based on priority rules for key tasks specifically includes: constructing a monitoring network monitoring efficiency evaluation index system; using the positions of all n monitoring stations in the radio monitoring network as particle coordinates; evaluating the deployment efficiency of the monitoring stations using a particle swarm optimization algorithm; obtaining the collaborative monitoring repetition rate; and adjusting the monitoring areas or monitoring targets of different monitoring stations in the radio monitoring network based on the evaluation results of the deployment efficiency of the monitoring stations.

7. The task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device according to claim 1, characterized in that, The unified encoding process for each task model specifically includes: first, constructing a task model based on the task type, which includes rapid continuous scanning of a specified frequency band, collaborative scanning of a specified frequency band, timed monitoring and listening, and radiation source localization; then, uniformly encoding the task parameters in the task model.

8. A task planning and loading system based on a miniature, easy-to-use electromagnetic sensing device, implemented using the task planning and loading method based on a miniature, easy-to-use electromagnetic sensing device as described in any one of claims 1 to 7, characterized in that, It includes a task model construction module, a task scenario association module, a solution recommendation module, and an instruction loading module; among which, The task model building module is used to uniformly sort out spectrum monitoring tasks, summarize various task models, and uniformly encode each task model to generate an instruction code table. The task scenario association module is used to generate dynamically configurable basic task templates for different target tasks and target stages. It selects basic task templates according to different task types and task stages, generates basic spectrum monitoring task requirements, and adds or removes different task modules. It also generates personalized task template sets according to different operating styles. The solution recommendation module is used to perform task planning based on a heuristic task planning algorithm with priority rules for key tasks, based on a set of personalized templates, and generate the best recommended task template. The instruction loading module is used to plan tasks based on recommended task templates. After the task planning is completed, it generates code-based task loading instructions based on the instruction code table and sends the code-based task loading instructions through an available communication link.

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