A distributed data monitoring and transmission system based on the Internet of Things

Abstract

The present invention discloses a distributed data monitoring and transmission system based on the Internet of Things, which relates to the technical field of the Internet of Things, and includes a data monitoring and processing center, wherein the data monitoring and processing center is communicatively connected with a data acquisition module, a data storage module, a load balancing module, a resource management module, a task scheduling module, and a user interface module, wherein the modules are connected by electrical signals; the data acquisition module is used to collect raw data including equipment performance parameters from heating equipment in the Internet of Things, and pre-process the raw data; the data storage module is used to store the collected data using distributed storage technology. The present invention improves the real-time and accuracy of data by real-time acquisition and transmission of equipment performance parameters, and the load balancing module and the resource management module work together to dynamically allocate computing and storage resources to cope with different data distributions and load requirements, thereby improving the overall performance and throughput of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things for heating systems, and in particular to a distributed data monitoring and transmission system based on the Internet of Things. Background Art

[0002] With the continuous increase in global energy demand and the pursuit of sustainable development, energy management and planning have become increasingly important. As an important area of ​​energy consumption, the efficiency and management level of heating are directly related to energy utilization efficiency and environmental protection. Traditional heating systems often lack real-time monitoring and intelligent control capabilities, resulting in energy waste and low heating efficiency. The Internet of Things technology can connect various heating equipment to realize real-time data collection and transmission, providing a technical basis for the intelligent management and optimization of heating systems.

[0003] Due to the large number of devices in the heating system Internet of Things, a large amount of data will be generated. During peak usage, resources will be unevenly distributed, which will have a great impact on data writing and querying. Therefore, how to distribute the computing tasks and storage resources in the heating system Internet of Things, and dynamically allocate and schedule resources based on the status and needs of the heating equipment in the Internet of Things to improve the efficiency and performance of the system is the problem we need to solve. To this end, a distributed data monitoring and transmission system based on the Internet of Things is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed data monitoring and transmission system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A distributed data monitoring and transmission system based on the Internet of Things, comprising a data monitoring and processing center, wherein the data monitoring and processing center is communicatively connected with a data acquisition module, a data storage module, a load balancing module, a resource management module, a task scheduling module and a user interface module, wherein electrical signals are connected between the modules; The data acquisition module is used to collect raw data including equipment performance parameters from the heating equipment in the Internet of Things, and pre-process the raw data. The equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration, to ensure the real-time and accuracy of the data, and provide a basis for subsequent data transmission and processing; The data storage module is used to store the collected data, including raw data and pre-processed data, using distributed storage technology to ensure data security and accessibility while improving storage efficiency; The load balancing module is used to analyze the pre-processed data and dynamically allocate computing and storage resources through load balancing technology to cope with different data distribution and load requirements, improve the overall performance and throughput of the system, and ensure the high availability and stability of the system; The resource management module is used to monitor and distribute the computing tasks and storage resources in the Internet of Things, and dynamically allocate and schedule resources according to the status and needs of the devices to improve the efficiency and performance of the system; The task scheduling module is used to allocate data processing tasks to matching computing resources to ensure that tasks can be completed efficiently and accurately while optimizing resource utilization and response time; The user interface module is used to provide users with a visual interface and data display function, so that users can intuitively understand the device status and environmental parameters, facilitate users to monitor device status, view data and analyze results, and improve system availability and user experience.

[0006] A further improvement of the technical solution of the present invention is that in the data acquisition module, the acquisition process of the equipment performance parameters includes: The data acquisition module establishes a connection with the sensors in the heating equipment in the Internet of Things through its interface and communication method (Wi-Fi), and presets the data acquisition frequency according to application requirements. The data acquisition frequency determines the rate of data collection from the heating equipment in the Internet of Things, thereby ensuring the real-time and accuracy of the data; According to the preset data collection frequency, raw data including equipment performance parameters are collected from the heating equipment in the Internet of Things, wherein the equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration; Preprocess the collected raw data, including filtering, denoising, normalization and data formatting; The processed data is verified to check the integrity of the data packet and the range of the data value to ensure that no data is lost or damaged, and the verified data is encapsulated to facilitate subsequent data transmission, and then the encapsulated data is transferred to the data storage module for storage.

[0007] A further improvement of the technical solution of the present invention is that: in the data storage module, the process of storing the collected data includes: The data storage module receives the original data and pre-processed data including environmental parameters and equipment performance parameters from the data acquisition module, and selects a storage node according to the data storage requirements; Data sharding technology is used to divide the preprocessed data into multiple parts, and multiple copies are created for each data shard, distributed on different storage nodes to improve data reliability and fault tolerance; Upload the data shards to the corresponding storage nodes and store the metadata related to each data shard (data identification, storage location, replica information, etc.) for subsequent data access and management.

[0008] A further improvement of the technical solution of the present invention is that: in the load balancing module, the process of dynamically allocating computing and storage resources includes: Traverse the pre-processed environmental parameters and equipment performance parameter data, analyze the data size, type, access frequency and computing requirements, and perform load assessment; Define indicators for resource status analysis, including CPU usage, memory usage, disk I / O, and network bandwidth, to monitor the status of computing and storage resources, and analyze resource utilization based on monitoring data to provide support for load balancing decisions; According to the results of data load evaluation and resource status analysis, a weighted round-robin load balancing strategy is selected to dynamically allocate computing and storage resources. Weighted round-robin is to assign different weights according to the performance differences of servers, and the weight value represents the server's ability to process requests. Monitor the performance of the load-balanced system, analyze the response time, throughput, and error rate indicators, and calculate the performance trend index based on the baseline value of each indicator to clarify the matching degree of the load-balancing strategy.

[0009] A further improvement of the technical solution of the present invention is that the process of clarifying the matching degree of the load balancing strategy includes: Regularly monitor the system's response time, throughput, and error rate, determine the system's baseline response time and baseline throughput, and obtain performance trend indexes; Analyze the value of the performance trend index to determine the matching degree of the load balancing strategy. If the performance trend index tends to 1, it means that the current load balancing strategy matches the system performance well. If the performance trend index tends to 0, it means that the current load balancing strategy does not match the system performance well and needs to be adjusted. According to the performance trend index value and performance monitoring results, adjust the load balancing strategy to optimize system performance.

[0010] A further improvement of the technical solution of the present invention is that: in the resource management module, the process of monitoring and distributed management of computing tasks and storage resources includes: Continuously monitor the usage of computing resources and storage resources in the system, track the status of heating equipment in the Internet of Things, and analyze the equipment performance parameters of heating equipment in the Internet of Things, where the equipment performance parameters include equipment temperature, equipment energy consumption, and equipment vibration; Based on the status tracking results of heating equipment in the Internet of Things, preset the benchmark values ​​of equipment performance parameters, analyze the differences between the actual values ​​of equipment performance parameters and their respective benchmark values, comprehensively calculate the equipment performance change index, and clarify the performance change trend of heating equipment in the Internet of Things; Combined with the performance change trend of heating equipment in the Internet of Things, the resource requirements of each device and application are analyzed, and the resource demand trend of heating equipment and applications in the Internet of Things is clarified, including computing demand and storage demand. Then a resource allocation strategy is formulated and implemented to dynamically allocate computing tasks and storage resources to the heating equipment in the Internet of Things. During the allocation process, the performance, location and network status of the equipment are comprehensively considered to ensure the effective utilization of resources and the smooth completion of tasks.

[0011] A further improvement of the technical solution of the present invention is that the process of formulating the resource allocation strategy includes: The performance change index of each device is calculated based on the device performance change index to quantify the performance deviation of the heating equipment in the Internet of Things. The resource requirements of each device and application are analyzed based on the device performance change index. Performance degradation means that more computing resources or storage resources are needed to maintain the service level. Utilize historical data and current performance change trends to predict the resource demand trends of various devices and applications, and formulate resource allocation strategies based on resource demand trends and device status. Resource allocation strategies include load balancing, resource reservation, and task migration. Load balancing means allocating tasks to devices with lower loads to avoid overloading certain devices. Resource reservation means reserving a certain amount of resources for critical tasks to ensure that they can still operate normally under high loads. Task migration means migrating tasks from one device to another to optimize resource utilization.

[0012] A further improvement of the technical solution of the present invention is that the process of implementing the resource allocation strategy includes: Analyze the computing and storage requirements of heating equipment, including data collection frequency, data processing type and storage data volume, and evaluate the status and capacity of currently available computing and storage resources; Classify tasks according to the characteristics of the heating equipment tasks (temperature control, energy consumption optimization) and set priorities; Combine the performance, location and cost-effectiveness of resources to associate the task requirements of heating equipment with the matching computing and storage resources, give priority to high-priority tasks, and monitor the execution status of tasks; Monitor the execution process of the task, including the progress of the calculation task and the status of the data storage, and collect the performance data of the task execution including processing time and resource utilization, and feed it back to the resource management module.

[0013] A further improvement of the technical solution of the present invention is that: in the task scheduling module, the process of allocating data processing tasks to matching computing resources includes: Receive data processing tasks requested by the data acquisition module or users, and parse the received task requests to extract the specific requirements and constraints of the tasks, including task type, urgency, execution time, estimated processing time and required resources; Monitor the status of currently available computing resources, including CPU load, memory usage, storage space, and network bandwidth, and analyze task requirements, classifying tasks according to their complexity, data volume, and processing requirements; According to the classification results, tasks are associated with matching computing resources, and resources are allocated to high-priority tasks first to ensure that key tasks can be executed first. According to the priority and execution time conditions of the tasks, task scheduling plans are formulated to ensure that tasks can be executed on time and efficiently, while avoiding resource conflicts and waste; Assign tasks to matching computing resources for execution, monitor the execution process of tasks to ensure that tasks can be executed correctly and achieve expected results, dynamically adjust the allocation of computing resources during task execution, release occupied computing resources for completed tasks so that they can be used for other tasks, monitor the response time during task execution to ensure that tasks can be completed within a reasonable time, and optimize and adjust tasks with long response times; Monitor the execution status of tasks in real time, including task progress and resource usage. For abnormal situations, issue alarms and handle them, and record the execution log of tasks, including task execution time, execution results, and abnormal information.

[0014] A further improvement of the technical solution of the present invention is that the user interface module specifically includes: Analyze user needs and determine the functions of the user interface, including device status display and data analysis results, and integrate backend data sources so that the user interface can obtain device status data on demand; Display the acquired equipment status data in real time, show the latest equipment status parameters to users, and provide historical data query function, allowing users to view past equipment status parameter records; Provides abnormal alarm and notification functions. When the device status is abnormal, it notifies the user, records user operation logs, and monitors the usage of the user interface for problem troubleshooting and performance optimization.

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides a distributed data monitoring and transmission system based on the Internet of Things, which improves the real-time and accuracy of data by real-time collection and transmission of equipment performance parameters. The load balancing module and the resource management module work together to dynamically allocate computing and storage resources to cope with different data distribution and load requirements, thereby improving the overall performance and throughput of the system.

[0016] 2. The present invention provides a distributed data monitoring and transmission system based on the Internet of Things. By monitoring and distributing the management of computing tasks and storage resources, dynamically allocating and scheduling resources according to device status and demand, the efficiency and performance of the system are improved. The dynamic resource allocation mechanism can ensure that resources are optimally configured within the system, avoid waste of resources, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a module diagram of the present invention.

[0019] Figure 2 Flowchart for dynamically allocating computing and storage resources for the present invention.

[0020] Figure 3 A flow chart for monitoring and distributed management of computing tasks and storage resources according to the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a distributed data monitoring and transmission system based on the Internet of Things, including a data monitoring and processing center, which is communicatively connected to a data acquisition module, a data storage module, a load balancing module, a resource management module, a task scheduling module and a user interface module, wherein electrical signals are connected between the modules.

[0023] The data acquisition module is used to collect raw data including equipment performance parameters from the heating equipment in the Internet of Things, and pre-process the raw data. The equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration, to ensure the real-time and accuracy of the data, and provide a basis for subsequent data transmission and processing. The data acquisition module establishes a connection with the sensors in the heating equipment in the Internet of Things through its interface and communication method (Wi-Fi), and presets the data acquisition frequency according to application requirements. The data acquisition frequency determines the rate of data collection from the heating equipment in the Internet of Things, thereby ensuring the real-time and accuracy of the data. According to the preset data acquisition frequency, the raw data including equipment performance parameters are collected from the heating equipment in the Internet of Things, where the equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration. The collected raw data The initial data is preprocessed, including filtering, denoising, normalization and data formatting operations, wherein filtering and denoising operations can remove noise and outliers in the data to make the data smoother and more accurate, normalization and data formatting operations can ensure the consistency and comparability of data between different devices and systems to improve data quality, and the preprocessed data is aggregated and compressed to merge multiple data points into one data point through aggregation operations, and the redundancy and duplication of data are reduced through compression operations to reduce the amount of data transmission and storage space, and the processed data is verified to check the integrity of the data packet and the range of data values ​​to ensure that no data is lost or damaged, and the verified data is encapsulated to facilitate subsequent data transmission, and then the encapsulated data is transmitted to the data storage module for storage.

[0024] The data storage module is used to store the collected data, including original data and pre-processed data, using distributed storage technology to ensure data security and accessibility while improving storage efficiency. The data storage module receives original data and pre-processed data including environmental parameters and equipment performance parameters from the data acquisition module, and selects a storage node based on the data storage requirements. It uses data sharding technology to divide the pre-processed data into multiple parts and creates multiple copies for each data shard, which are distributed on different storage nodes to improve data reliability and fault tolerance. It uploads the data shards to the corresponding storage nodes and stores metadata related to each data shard (data identification, storage location, copy information, etc.) for subsequent data access and management.

[0025] The load balancing module is used to analyze the preprocessed data and dynamically allocate computing and storage resources through load balancing technology to cope with different data distributions and load requirements, improve the overall performance and throughput of the system, ensure the high availability and stability of the system, traverse the preprocessed environmental parameters and equipment performance parameter data, analyze the scale, type, access frequency and computing requirements of the data, perform load evaluation, and clarify the indicators of resource status analysis, including CPU usage, memory usage, disk I / O and network bandwidth, and then monitor the status of computing and storage resources. According to the monitoring data, analyze the utilization of resources to provide support for load balancing decisions. According to the results of data load evaluation and resource status analysis, select the weighted polling load balancing strategy to dynamically allocate computing and storage resources. Among them, weighted polling is based on Different weights are assigned to the performance differences of servers. The weight value represents the server's ability to process requests. The performance of the load-balanced system is monitored, and the response time, throughput, and error rate indicators are analyzed. Combined with the baseline values ​​of each indicator, the performance trend index is calculated to clarify the matching degree of the load balancing strategy. The response time, throughput, and error rate of the system are monitored regularly, and the baseline response time and baseline throughput of the system are determined. The performance trend index is obtained, and the value of the performance trend index is analyzed to determine the matching degree of the load balancing strategy. If the performance trend index tends to 1, it means that the current load balancing strategy matches the system performance well. If the performance trend index tends to 0, it means that the current load balancing strategy does not match the system performance well and needs to be adjusted. According to the value of the performance trend index and the performance monitoring results, the load balancing strategy is adjusted to optimize system performance.

[0026] Furthermore, the expression of the performance trend index is:

[0027] In the formula, It is a performance trend index that measures the difference between current performance and ideal performance. is the actual response time, i.e. the actual time to process the request, is the baseline response time, a performance indicator used to compare the actual response time, that is, the average response time under the baseline state. is the actual throughput, that is, the number of requests processed per unit time, is the baseline throughput, a performance indicator used to compare the actual throughput, that is, the average throughput under the baseline state. is the error rate, that is, the proportion of requests with errors per unit time, The value range is between 0 and 1. The closer the value is to 1, the closer the performance is to the baseline state. The closer the value is to 0, the worse the performance is. Increase (response time becomes longer) or Decrease (throughput decreases) or When the error rate increases, The value will decrease, indicating that the performance tends to deteriorate. Reduce (shorter response time), Increase (throughput increases) or When the error rate decreases, The value of will increase, indicating that the performance is getting better.

[0028] The resource management module is used to monitor and distribute the computing tasks and storage resources in the Internet of Things, and dynamically allocate and schedule resources according to the status and needs of the equipment to improve the efficiency and performance of the system.

[0029] The task scheduling module is used to allocate data processing tasks to matching computing resources to ensure that tasks can be completed efficiently and accurately while optimizing resource utilization and response time.

[0030] The user interface module is used to provide users with a visual interface and data display functions, so that users can intuitively understand the device status and environmental parameters, facilitate users to monitor device status, view data and analyze results, and improve system availability and user experience.

[0031] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the resource management module, the process of monitoring and distributed management of computing tasks and storage resources includes: Continuously monitor the usage of computing resources and storage resources in the system, track the status of heating equipment in the Internet of Things, analyze the equipment performance parameters of heating equipment in the Internet of Things, where the equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration. Based on the status tracking results of heating equipment in the Internet of Things, preset the benchmark values ​​of equipment performance parameters, analyze the differences between the actual values ​​of equipment performance parameters and their respective benchmark values, comprehensively calculate the equipment performance change index, clarify the performance change trend of heating equipment in the Internet of Things, combine the performance change trend of heating equipment in the Internet of Things, analyze the resource requirements of each device and application, clarify the resource demand trend of heating equipment and applications in the Internet of Things, including computing requirements and storage requirements, and then formulate and implement resource allocation strategies, dynamically allocate computing tasks and storage resources to heating equipment in the Internet of Things, comprehensively consider the performance, location and network status of the equipment during the allocation process to ensure the effective use of resources and the smooth completion of tasks, calculate the performance change index of each device according to the equipment performance change index, quantify the deviation of the performance of heating equipment in the Internet of Things, and analyze the resource requirements of each device and application according to the equipment performance change index. Performance degradation means that more computing resources or storage resources are needed to maintain Service level, using historical data and current performance change trends, predict the resource demand trends of various devices and applications, and formulate resource allocation strategies based on resource demand trends and device status. Among them, resource allocation strategies include load balancing, resource reservation, and task migration. Load balancing means allocating tasks to devices with lower loads to avoid overloading some devices. Resource reservation means reserving a certain amount of resources for critical tasks to ensure that they can still operate normally under high load. Task migration means migrating tasks from one device to another to optimize resource utilization. Analyze the computing and storage requirements of heating equipment, including data collection frequency, data processing type, and storage data volume, evaluate the status and capacity of currently available computing resources and storage resources, classify tasks according to the task characteristics of heating equipment (temperature control, energy consumption optimization), and set priorities. Comprehensively consider the performance, location, and cost-effectiveness of resources, associate the task requirements of heating equipment with matching computing and storage resources, give priority to high-priority tasks, monitor the execution status of tasks, monitor the execution process of tasks, including the progress of computing tasks and the status of data storage, and collect performance data of task execution including processing time and resource utilization, and feed it back to the resource management module.

[0032] Furthermore, the expression of the equipment performance variation index is:

[0033] In the formula, It is the equipment performance change index, which is used to measure the degree of change of equipment performance relative to the benchmark value. is the actual value of the device temperature, is the reference value of the device temperature, is the actual value of the equipment’s energy consumption, is the baseline value of equipment energy consumption, is the actual value of the equipment vibration, is the reference value of equipment vibration, The value range is between 0 and 1. The closer the value is to 1, the closer the device performance is to its benchmark value, and the performance change is small. The closer the value is to 0, the greater the difference between the device performance and the benchmark value, and the performance change is significant. , or with their respective baseline values , or When the difference is not big, The value of will be close to 1, indicating that the device performance is stable. On the contrary, if , or If any one or more of them differs greatly from their baseline values, The value of will decrease, indicating a significant change in device performance.

[0034] In the task scheduling module, the process of allocating data processing tasks to matching computing resources includes: Receive data processing tasks requested by the data acquisition module or users, parse the received task requests, extract the specific requirements and constraints of the tasks, including task type, urgency, execution time, estimated processing time and required resources, monitor the status of currently available computing resources, including CPU load, memory usage, storage space and network bandwidth, analyze the requirements of the tasks, classify the tasks according to their complexity, data volume and processing requirements, associate the tasks with matching computing resources according to the classification results, allocate resources to high-priority tasks first, ensure that key tasks can be executed first, and formulate task scheduling plans based on the priority and execution time conditions of the tasks. The scheduling plans ensure that the tasks can be It can execute tasks on time and efficiently while avoiding resource conflicts and waste. It allocates tasks to matching computing resources for execution and monitors the execution process of tasks to ensure that tasks can be executed correctly and achieve expected results. During the execution of tasks, it dynamically adjusts the allocation of computing resources. For completed tasks, it releases occupied computing resources so that they can be used for other tasks. It also monitors the response time during the execution of tasks to ensure that tasks can be completed within a reasonable time. For tasks with long response times, it makes optimization adjustments and monitors the execution status of tasks in real time, including task progress and resource usage. For abnormal situations, it issues alarms and handles them, and records the execution log of tasks, including task execution time, execution results, and abnormal information.

[0035] The user interface module specifically includes: Analyze user needs, determine the functions of the user interface, including device status display and data analysis results, etc., and integrate the back-end data source so that the user interface can obtain device status data on demand, display the acquired device status data in real time, show the latest device status parameters to users, and provide historical data query function, allowing users to view past device status parameter records, provide abnormal alarm and notification functions, notify users when the device status is abnormal, and record user operation logs, monitor the usage of the user interface for problem troubleshooting and performance optimization.

[0036] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed data monitoring and transmission system based on the Internet of Things, including a data monitoring processing center, characterized in that: The data monitoring and processing center is communicatively connected with a data acquisition module, a data storage module, a load balancing module, a resource management module, a task scheduling module and a user interface module, wherein the modules are connected by electrical signals; The data acquisition module is used to collect raw data including equipment performance parameters from the heating equipment in the Internet of Things, and pre-process the raw data; The data storage module is used to store the collected data using distributed storage technology; The load balancing module is used to analyze the pre-processed data and dynamically allocate computing and storage resources through load balancing technology to cope with different data distribution and load requirements; The resource management module is used to monitor and distribute the computing tasks and storage resources in the Internet of Things, and dynamically allocate and schedule resources according to the status and needs of the devices; The task scheduling module is used to allocate data processing tasks to matching computing resources; The user interface module is used to provide users with a visual interface and data display functions.

2. According to claim 1, a distributed data monitoring and transmission system based on the Internet of Things is characterized in that: In the data acquisition module, the process of acquiring equipment performance parameters includes: The data acquisition module establishes a connection with the sensors in the heating equipment in the Internet of Things through its interface and communication method, and presets the data acquisition frequency according to application requirements; According to the preset data collection frequency, raw data including equipment performance parameters are collected from the heating equipment in the Internet of Things, wherein the equipment performance parameters include equipment temperature, equipment energy consumption and equipment vibration; Preprocess the collected raw data, including filtering, denoising, normalization and data formatting; The processed data is verified to check the integrity of the data packet and the range of the data value to ensure that no data is lost or damaged, and the verified data is encapsulated and then transmitted to the data storage module for storage.

3. The distributed data monitoring and transmission system based on the Internet of Things according to claim 2 is characterized in that: In the data storage module, the process of storing the collected data includes: The data storage module receives the original data and pre-processed data including environmental parameters and equipment performance parameters from the data acquisition module, and selects a storage node according to the data storage requirements; Using data sharding technology, the pre-processed data is divided into multiple parts, and multiple copies are created for each data shard, distributed on different storage nodes; Upload the data shards to the corresponding storage nodes and store the metadata related to each data shard.

4. The distributed data monitoring and transmission system based on the Internet of Things according to claim 3 is characterized in that: In the load balancing module, the process of dynamically allocating computing and storage resources includes: Traverse the pre-processed environmental parameters and equipment performance parameter data, analyze the data size, type, access frequency and computing requirements, and perform load assessment; Define indicators for resource status analysis, including CPU usage, memory usage, disk I / O, and network bandwidth, to monitor the status of computing and storage resources, and analyze resource utilization based on monitoring data; According to the results of data load evaluation and resource status analysis, a weighted round-robin load balancing strategy is selected to dynamically allocate computing and storage resources. Monitor the performance of the load-balanced system, analyze the response time, throughput, and error rate indicators, and calculate the performance trend index based on the baseline value of each indicator to clarify the matching degree of the load-balancing strategy.

5. The distributed data monitoring and transmission system based on the Internet of Things according to claim 4 is characterized in that: The process of clarifying the matching degree of the load balancing strategy includes: Regularly monitor the system's response time, throughput, and error rate, determine the system's baseline response time and baseline throughput, and obtain performance trend indexes; Analyze the value of the performance trend index to determine the matching degree of the load balancing strategy. If the performance trend index tends to 1, it means that the current load balancing strategy matches the system performance well. If the performance trend index tends to 0, it means that the current load balancing strategy does not match the system performance well and needs to be adjusted. According to the performance trend index value and performance monitoring results, adjust the load balancing strategy to optimize system performance.

6. The distributed data monitoring and transmission system based on the Internet of Things according to claim 5 is characterized in that: In the resource management module, the process of monitoring and distributed management of computing tasks and storage resources includes: Continuously monitor the usage of computing resources and storage resources in the system, track the status of heating equipment in the Internet of Things, and analyze the equipment performance parameters of heating equipment in the Internet of Things, where the equipment performance parameters include equipment temperature, equipment energy consumption, and equipment vibration; Based on the status tracking results of heating equipment in the Internet of Things, preset the benchmark values ​​of equipment performance parameters, analyze the differences between the actual values ​​of equipment performance parameters and their respective benchmark values, comprehensively calculate the equipment performance change index, and clarify the performance change trend of heating equipment in the Internet of Things; Combined with the performance change trend of heating equipment in the Internet of Things, the resource requirements of each device and application are analyzed, and the resource demand trend of heating equipment and applications in the Internet of Things is clarified, including computing demand and storage demand. Then a resource allocation strategy is formulated and implemented to dynamically allocate computing tasks and storage resources to the heating equipment in the Internet of Things.

7. The distributed data monitoring and transmission system based on the Internet of Things according to claim 6 is characterized in that: The process of formulating a resource allocation strategy includes: Calculate the performance change index of each device based on the device performance change index, quantify the deviation of heating equipment performance in the Internet of Things, and analyze the resource requirements of each device and application based on the device performance change index; Utilize historical data and current performance change trends to predict resource demand trends for each device and application, and formulate resource allocation strategies based on resource demand trends and device status. Resource allocation strategies include load balancing, resource reservation, and task migration.

8. The distributed data monitoring and transmission system based on the Internet of Things according to claim 7 is characterized in that: The process of implementing the resource allocation strategy includes: Analyze the computing and storage requirements of heating equipment, including data collection frequency, data processing type and storage data volume, and evaluate the status and capacity of currently available computing and storage resources; Classify tasks according to the characteristics of heating equipment tasks and set priorities; Combine the performance, location and cost-effectiveness of resources to associate the task requirements of heating equipment with the matching computing and storage resources, give priority to high-priority tasks, and monitor the execution status of tasks; Monitor the execution process of the task, including the progress of the calculation task and the status of the data storage, and collect the performance data of the task execution including processing time and resource utilization, and feed it back to the resource management module.

9. The distributed data monitoring and transmission system based on the Internet of Things according to claim 8 is characterized in that: In the task scheduling module, the process of allocating data processing tasks to matching computing resources includes: Receive data processing tasks requested by the data acquisition module or users, and parse the received task requests to extract the specific requirements and constraints of the tasks, including task type, urgency, execution time, estimated processing time and required resources; Monitor the status of currently available computing resources, including CPU load, memory usage, storage space, and network bandwidth, and analyze task requirements, classifying tasks according to their complexity, data volume, and processing requirements; According to the classification results, the tasks are associated with matching computing resources, and a task scheduling plan is formulated according to the task priority and execution time conditions; Allocate tasks to matching computing resources for execution and monitor the execution process of tasks; Monitor the execution status of tasks in real time, including task progress and resource usage. For abnormal situations, issue alarms and handle them, and record the execution log of tasks, including task execution time, execution results, and abnormal information.

10. The distributed data monitoring and transmission system based on the Internet of Things according to claim 9, characterized in that: The user interface module specifically includes: Analyze user needs and determine the functions of the user interface, including device status display and data analysis results, and integrate backend data sources so that the user interface can obtain device status data on demand; Display the acquired equipment status data in real time, show the latest equipment status parameters to users, and provide historical data query function, allowing users to view past equipment status parameter records; Provides abnormal alarm and notification functions. When the device status is abnormal, it notifies the user, records the user operation log, and monitors the usage of the user interface.

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