Data acquisition and automatic control system for intelligent exhibition hall of science and technology museum

Abstract

The invention discloses a data acquisition and automatic control system for an intelligent exhibition hall of a science and technology museum, and relates to the technical field of intelligent control, the system comprises a load balancing module used for receiving data processing requests from a plurality of interaction systems and distributing computing resources through a load balancing algorithm, and a resource distribution module used for distributing the computing resources through a load balancing algorithm. The system comprises an allocation module used for adjusting an allocation strategy of computing resources based on historical data and real-time feedback and optimizing the response speed of the system, a monitoring module used for monitoring the operation state of each interactive device in an exhibition hall in real time and performing fault prediction, and a parameter adjustment module used for dynamically adjusting operation parameters of the interactive devices according to monitoring results and prediction data. According to the data acquisition and automatic control system for the intelligent exhibition hall of the science and technology museum, the problem of slow response of an exhibition hall interaction system caused by insufficient computing resources under the condition of concurrent operation of multiple devices in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a data acquisition and automation control system for an intelligent exhibition hall of a science and technology museum. Background Art

[0002] In modern science and technology museums, the application of smart exhibition halls has become an important part of displaying high-tech and interactive experiences. These exhibition halls are usually equipped with a large number of smart devices, including sensors, display screens, and automated control systems, which are used to achieve real-time data collection, interactive display, lighting control, and multimedia content playback. These systems usually rely on integrated computing platforms for data processing and task allocation to ensure that multiple devices can work together.

[0003] When implementing the above functions, the existing intelligent exhibition hall management system generally relies on a central controller or server to process various types of data, including equipment status monitoring, real-time data collection, and user interaction response. However, when multiple devices are running concurrently, the existing system faces the problem of insufficient computing resource allocation. This resource bottleneck usually leads to a decrease in system responsiveness, which is manifested as a response delay of the interactive system or even an inability to process user instructions in a timely manner. For example, when multiple users operate the exhibition hall interactive equipment at the same time, the central controller may be unable to effectively dispatch resources due to overload operation, resulting in a slow response speed of the exhibition equipment and a significant decrease in user experience. In addition, in order to cope with equipment failures or changes in user needs, existing technologies usually rely on manual intervention for parameter adjustment or system optimization, which further reduces the intelligence and efficiency of the system. With the expansion of the scale of science and technology museum exhibition halls and the increase in the number of equipment, it is difficult for existing technologies to effectively adapt to complex and changing operational needs. Summary of the invention

[0004] The purpose of the present invention is to provide a data acquisition and automation control system for a smart exhibition hall of a science and technology museum, so as to solve the problem of slow response of the exhibition hall interactive system due to insufficient computing resources when multiple devices are running concurrently in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a data acquisition and automation control system for a smart exhibition hall of a science and technology museum, the system comprising: A load balancing module, which is used to receive data processing requests from multiple interactive systems and allocate computing resources through a load balancing algorithm; The resource allocation module connected to the load balancing module is used to adjust the allocation strategy of computing resources based on historical data and real-time feedback to optimize the system response speed, including dividing the equipment data processing request tasks in the smart exhibition hall into first priority and second priority, defining the ratio p of resources allocated to the first priority tasks and the ratio q of resources allocated to the second priority tasks, and dynamically adjusting the ratio of p and q according to task requirements so that the formula p 2 +2pq+q 2 =1 holds, where p represents the proportion of resources allocated to the first priority task, and q represents the proportion of resources allocated to the second priority task; The monitoring module connected to the resource allocation module is used to monitor the operating status of each interactive device in the exhibition hall in real time and predict faults; The parameter adjustment module connected to the resource allocation module and the monitoring module is used to dynamically adjust the operating parameters of the interactive device according to the monitoring results and prediction data.

[0006] Preferably, the load balancing module receives data requests from multiple interactive systems and allocates computing resources through a load balancing algorithm, including: Calculate the current task processing speed of the equipment in the smart exhibition hall. The specific formula is: ; Among them, v represents the current task processing speed, v max represents the maximum processing speed under ideal conditions, ρ represents the number of tasks assigned to the device per unit time, and ρ max Indicates the number of tasks assigned to the device per unit time at the performance limit of the device; Calculate the amount of tasks completed by each device per unit time. The specific formula is: u=v×ρ; Among them, u represents the amount of tasks completed by the device per unit time, v represents the current task processing speed, and ρ represents the number of tasks assigned to the device per unit time; Then adjust the task allocation strategy, update the number of tasks assigned to the device per unit time in real time, and recalculate the allocation strategy to achieve a balanced distribution of task flows.

[0007] Preferably, the monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction, including: Initialize the health status of the equipment in the smart exhibition hall, record the start time of the equipment, calculate the health status of the equipment in real time, and monitor the fluctuation of the failure rate. The specific formula is: ; Where N(t) represents the degree of change of the health status of the equipment within time t, t represents time, N0 represents the health status of the equipment at the beginning of operation, λ represents the equipment failure rate, and e represents the base of the natural logarithm; Set the threshold T h , when N(t)≤T h When the fault occurs, an early warning is issued to indicate that the equipment is about to fail.

[0008] Preferably, the parameter adjustment module dynamically adjusts the operating parameters of the interactive device according to the monitoring results and the prediction data, including: Collect the current task load of the equipment in the smart exhibition hall, determine the proportional coefficient based on the hardware performance, and calculate the adjustment rate of the equipment operation parameters. The specific formula is: ; Among them, R represents the adjustment rate of the equipment's operating parameters, a represents the proportional coefficient, M represents the current task load of the equipment, and b represents the efficiency coefficient of the equipment adjustment; According to the calculation results of the rate adjustment of the equipment's operating parameters, adjust the equipment's operating parameters to adapt to the load requirements, record the adjustment results, optimize the values ​​of the efficiency coefficient and proportional coefficient of the equipment adjustment, and improve the adjustment accuracy.

[0009] Preferably, the load balancing module receives data requests from multiple interactive systems, and allocates computing resources through a load balancing algorithm. The calculation formula for the number of tasks ρ allocated to the device per unit time is: ρ=E / F; Among them, ρ represents the number of tasks assigned to the device per unit time, E represents the task request, and F represents the device response time.

[0010] Preferably, the load balancing module receives data requests from multiple interactive systems, and allocates computing resources through a load balancing algorithm and a task allocation strategy that preferentially allocates tasks with a first priority.

[0011] Preferably, the monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction. The monitoring module evaluates the degree of change of device performance during operation time by calculating the attenuation amplitude of the device health status. The specific calculation formula is: ΔN=N0-N(t); Among them, ΔN represents the attenuation amplitude of the equipment health status, N0 represents the health status of the equipment at the beginning of operation, and N(t) represents the degree of change of the equipment health status within time t.

[0012] Preferably, the monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs a calculation formula for the device failure rate λ in the failure prediction: λ=λ0+k×G; Among them, λ represents the equipment failure rate, λ0 represents the basic failure rate of the equipment, k represents the environmental sensitivity coefficient, and G represents the external environmental variables that affect the equipment failure rate.

[0013] Preferably, the resource allocation module includes a feedback optimization unit for dynamically adjusting the resource allocation strategy by comparing the real-time task processing efficiency with the expected efficiency.

[0014] Preferably, the monitoring module can generate periodic health status reports, recording in detail the health status change trends of the equipment.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The data acquisition and automation control system of the science and technology museum's smart exhibition hall receives data processing requests from multiple interactive systems through a load balancing module, and allocates computing resources through a load balancing algorithm. The resource allocation module adjusts the computing resource allocation strategy based on historical data and real-time feedback to optimize the system response speed. The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and predicts faults. The parameter adjustment module dynamically adjusts the operating parameters of the interactive device according to the monitoring results and predicted data, solving the problem of response delay caused by insufficient computing resource allocation in the prior art, enabling the interactive devices in the smart exhibition hall to maintain efficient response in the case of concurrent multi-user operations, improving user experience, making full use of the computing power of each device, and avoiding the central controller or server from being slowed down due to overload. Low overall operating efficiency, ensuring the stability and efficiency of system operation, introducing real-time monitoring module and parameter adjustment module, realizing real-time monitoring and automatic adjustment of equipment operation status, and adapting to equipment failure or changes in user needs without relying on human intervention, significantly improving the intelligence level of the system, reducing the risk of system interruption due to equipment failure, and providing guarantee for the continuous and reliable operation of the exhibition hall, optimizing the system architecture to make it flexible and scalable, able to efficiently adapt to complex and changeable operating requirements, reducing the frequency and complexity of human intervention, reducing the cost of system operation and maintenance, and at the same time improving the reliability of system operation, solving the problem of slow response of the exhibition hall interactive system due to insufficient computing resources when multiple devices are running concurrently in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0018] like Figure 1As shown, the present invention provides a technical solution: a data acquisition and automation control system for a smart exhibition hall of a science and technology museum, comprising: A load balancing module, which is used to receive data processing requests from multiple interactive systems and allocate computing resources through a load balancing algorithm; The resource allocation module connected to the load balancing module is used to adjust the allocation strategy of computing resources based on historical data and real-time feedback to optimize the system response speed, including dividing the equipment data processing request tasks in the smart exhibition hall into first priority and second priority, defining the ratio p of resources allocated to the first priority tasks and the ratio q of resources allocated to the second priority tasks, and dynamically adjusting the ratio of p and q according to task requirements so that the formula p 2 +2pq+q 2 =1 holds, where p represents the proportion of resources allocated to the first priority task, and q represents the proportion of resources allocated to the second priority task; The monitoring module connected to the resource allocation module is used to monitor the operating status of each interactive device in the exhibition hall in real time and predict faults; The parameter adjustment module connected to the resource allocation module and the monitoring module is used to dynamically adjust the operating parameters of the interactive device according to the monitoring results and prediction data.

[0019] In this implementation, the load balancing module monitors the real-time data processing requests of multiple interactive systems and uses the load balancing algorithm to dynamically allocate computing resources to ensure that the computing needs of each interactive system can be reasonably responded to. The resource allocation module is based on historical data and real-time feedback according to the optimization formula p 2 +2pq+q 2 =1 dynamically adjusts the resource allocation ratio so that the system can give priority to the computing needs of important tasks (first priority tasks) while taking into account the processing efficiency of secondary tasks (second priority tasks). Through the monitoring module connected to the resource allocation module, the system can monitor the operating status of interactive devices in the exhibition hall in real time, detect potential operating anomalies or failures, and provide real-time warnings. The parameter adjustment module dynamically optimizes the equipment operating parameters based on the fault prediction results provided by the monitoring module to maintain system stability and reliability. This implementation method can effectively improve the system's response speed and resource utilization efficiency for multi-task processing by introducing a load balancing module and a resource allocation module, and avoid system performance degradation due to overload of a single device or task. At the same time, through the collaboration of the monitoring module and the parameter adjustment module, real-time monitoring and dynamic adjustment of the equipment operating status are achieved, which effectively reduces the equipment failure rate and improves the safety and stability of the system operation. The optimization formula for resource allocation provides a flexible control mechanism to ensure the adaptability of the system under different task requirements.

[0020] The load balancing module receives data requests from multiple interactive systems and allocates computing resources through a load balancing algorithm including: Calculate the current task processing speed of the equipment in the smart exhibition hall. The specific formula is: ; Among them, v represents the current task processing speed, v max represents the maximum processing speed under ideal conditions, ρ represents the number of tasks assigned to the device per unit time, and ρ max Indicates the number of tasks assigned to the device per unit time at the performance limit of the device; Calculate the amount of tasks completed by each device per unit time. The specific formula is: u=v×ρ; Among them, u represents the amount of tasks completed by the device per unit time, v represents the current task processing speed, and ρ represents the number of tasks assigned to the device per unit time; Then adjust the task allocation strategy, update the number of tasks assigned to the device per unit time in real time, and recalculate the allocation strategy to achieve a balanced distribution of task flows.

[0021] In this implementation, the system calculates the task processing speed v of each device in the smart exhibition hall and the amount of tasks completed per unit time u through the load balancing module to optimize the efficiency of task allocation. It shows that when the task load approaches the performance limit of the device (that is, ρ approaches ρmax), the task processing speed of the device will be significantly reduced, thereby preventing the device from being overloaded. On this basis, combined with the task volume formula u=v×ρ, the system dynamically adjusts the number of tasks assigned per unit time to ensure the balanced distribution of task flows of each device and maximize resource utilization. This implementation method can effectively avoid device overload and improve the rationality and balance of task allocation by introducing a dynamic calculation formula for device task processing speed and task completion volume. The processing speed formula provides a task control method based on the performance limit of the device, which can not only ensure efficient task processing, but also extend the service life of the equipment. The mechanism of real-time updating of task allocation strategy further optimizes the system response speed and overall efficiency, ensuring that the interactive devices in the smart exhibition hall are always in an efficient operating state.

[0022] The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and predicts faults, including: Initialize the health status of the equipment in the smart exhibition hall, record the start time of the equipment, calculate the health status of the equipment in real time, and monitor the fluctuation of the failure rate. The specific formula is: ; Where N(t) represents the degree of change of the health status of the equipment within time t, t represents time, N0 represents the health status of the equipment at the beginning of operation, λ represents the equipment failure rate, and e represents the base of the natural logarithm; Set the threshold T h , when N(t)≤T h When the fault occurs, an early warning is issued to indicate that the equipment is about to fail.

[0023] In this embodiment, the monitoring module records the initial health state N0 and the failure rate λ of the device and dynamically calculates the health state N(t) of the device in combination with the running time t. It reflects the process of equipment performance gradually declining over time. When the equipment health status N(t) drops to the set threshold T h When the following conditions occur, the system triggers an early warning to alert the operator that the equipment is about to fail or needs maintenance. By monitoring the equipment status in real time, the system can dynamically adjust the equipment operating parameters or task allocation to delay performance degradation and reduce the risk of failure. This implementation method realizes real-time monitoring of the equipment operating status and failure prediction by introducing a dynamic health status assessment model and early warning mechanism. Formula It provides a scientific and efficient method to quantify the health status of equipment and predict its operating life. h This method enables the system to detect potential problems in a timely manner and take preventive maintenance measures, thereby reducing equipment failure rates, extending equipment service life, and improving the stability and safety of the overall operation of the exhibition hall.

[0024] The parameter adjustment module dynamically adjusts the operating parameters of the interactive device according to the monitoring results and prediction data, including: Collect the current task load of the equipment in the smart exhibition hall, determine the proportional coefficient based on the hardware performance, and calculate the adjustment rate of the equipment operation parameters. The specific formula is: ; Among them, R represents the adjustment rate of the equipment's operating parameters, a represents the proportional coefficient, M represents the current task load of the equipment, and b represents the efficiency coefficient of the equipment adjustment; According to the calculation results of the rate adjustment of the equipment's operating parameters, adjust the equipment's operating parameters to adapt to the load requirements, record the adjustment results, optimize the values ​​of the efficiency coefficient and proportional coefficient of the equipment adjustment, and improve the adjustment accuracy.

[0025] In this embodiment, the parameter adjustment module collects the task load M of the device in real time and combines the hardware performance characteristics to use the formula Dynamically calculate the adjustment rate R of the device operating parameters. Among them, the proportionality coefficient a reflects the basic characteristics of the device performance, and the efficiency coefficient b indicates the response sensitivity of the device under different load conditions. By adjusting the device operating parameters, the system can better adapt to the real-time load changes in the exhibition hall and ensure the stability and efficiency of the interactive device in different usage scenarios. In addition, the adjustment results will be recorded and used for subsequent optimization of the proportionality coefficient a and the efficiency coefficient b, thereby gradually improving the accuracy of parameter adjustment. This implementation method uses the formula Determining the adjustment rate of operating parameters provides an accurate and efficient dynamic adjustment mechanism. The method of real-time recording and optimizing adjustment results can gradually improve the accuracy and sensitivity of equipment parameter adjustment, so as to better adapt to the load change requirements of exhibition hall equipment. The automated process of equipment operating parameter adjustment not only improves the response speed and adaptability of the system, but also significantly reduces the necessity of manual intervention, and improves the overall operating efficiency and service life of exhibition hall equipment.

[0026] The load balancing module receives data requests from multiple interactive systems and allocates computing resources through the load balancing algorithm. The calculation formula for the number of tasks ρ allocated to the device per unit time is: ρ=E / F; Among them, ρ represents the number of tasks assigned to the device per unit time, E represents the task request, and F represents the device response time.

[0027] In this embodiment, the load balancing module receives task requests from multiple interactive systems and uses the formula ρ=E / F to dynamically calculate the number of tasks allocated to each device per unit time. Among them, the number of task requests E reflects the current load demand of the system, and the device response time F indicates the processing capacity of the device. When the number of task requests E increases or the device response time F is extended, the calculated number of task allocations ρ will be adjusted accordingly to ensure that the task allocation meets the processing capacity of the device and avoid overload operation or waste of resources. This dynamic allocation process realizes the reasonable scheduling of system resources and optimizes the overall operating efficiency. By introducing the formula ρ=E / F, this embodiment can effectively dynamically adjust the number of task allocations according to the number of task requests and the device response time, ensure that the task allocation meets the processing capacity of the device, and avoid performance degradation or failure problems caused by task overload in the system. At the same time, the dynamic adjustment mechanism of task allocation improves the utilization efficiency of system resources, reduces the phenomenon of idle resource waste, and thus improves the operating efficiency and stability of the exhibition hall interactive equipment.

[0028] The load balancing module receives data requests from multiple interactive systems, and allocates computing resources in a load balancing algorithm to prioritize tasks with the first priority. In this embodiment, the load balancing module receives data requests from multiple interactive systems and adopts a priority-based task allocation strategy to prioritize tasks with the first priority. Specifically, the system classifies task requests according to priority, where the first priority task is a critical task and its computing resources need to be allocated first. The load balancing module detects the availability of computing resources in real time, first allocates resources to meet the computing requirements of the first priority task, and then allocates the remaining resources to the second priority or other low priority tasks. This strategy ensures that critical tasks can be processed in a timely manner, thereby improving the reliability and response efficiency of the system. By introducing a priority task allocation strategy, this embodiment can ensure that critical tasks (first priority tasks) are given priority in the allocation of system computing resources, thereby avoiding the occurrence of critical task delays or failures due to insufficient resources. At the same time, the optimization strategy for resource allocation takes into account the needs of tasks of different priorities, improves the flexibility of task processing and the efficiency of system operation. This mechanism significantly improves the response speed and stability of the interactive equipment in the exhibition hall, and ensures the continuity of user experience and the reliability of exhibition hall operation.

[0029] The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction. The monitoring module evaluates the degree of change of device performance during operation by calculating the attenuation amplitude of the device health status. The specific calculation formula is: ΔN=N0-N(t); Among them, ΔN represents the attenuation amplitude of the equipment health status, N0 represents the health status of the equipment at the beginning of operation, and N(t) represents the degree of change of the equipment health status within time t.

[0030] In this embodiment, the monitoring module calculates the attenuation amplitude ΔN of the health state during the operation time of the equipment by recording the initial health state N0 and the real-time health state N(t) of the equipment. The health state attenuation formula ΔN=N0-N(t) shows the changes in equipment performance over time. By analyzing the attenuation amplitude, the system can evaluate the degree of decline in equipment performance and infer the future operation status of the equipment in combination with the attenuation rate. When the attenuation amplitude ΔN exceeds the set threshold, the system will trigger an early warning, prompting the operator to maintain or replace the equipment in time, thereby avoiding the impact of equipment failure on the operation of the exhibition hall. This embodiment provides a simple and effective method for evaluating equipment performance by calculating the attenuation amplitude ΔN of the health state of the equipment. The use of the formula can quickly quantify the changes in equipment performance and provide data support for equipment failure prediction. Combined with the real-time monitoring and early warning mechanism, the system can promptly detect potential problems and take preventive maintenance measures to avoid system operation failures caused by equipment performance degradation. At the same time, the method has high computational efficiency and adaptability, and can operate stably under conditions of limited resources.

[0031] The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction. The calculation formula of the equipment failure rate λ is: λ=λ0+k×G; Among them, λ represents the equipment failure rate, λ0 represents the basic failure rate of the equipment, k represents the environmental sensitivity coefficient, and G represents the external environmental variables that affect the equipment failure rate.

[0032] In this implementation, the monitoring module collects the operating status of each interactive device in the exhibition hall and the external environmental variable G in real time, and calculates the failure rate λ of the device by combining the formula λ=λ0+k×G. Among them, the basic failure rate λ0 reflects the average failure probability of the device under a standard environment, the environmental sensitivity coefficient k represents the sensitivity of the device to external environmental changes (such as temperature, humidity, vibration, etc.), and the external environmental variable G represents the specific influencing factors of the current environment. By dynamically calculating the failure rate of the device, the system can predict the possibility of failure of the device under specific operating conditions, providing a reference for equipment maintenance and task scheduling. This implementation can effectively quantify the failure risk of the device by introducing the failure rate calculation formula λ=λ0+k×G, and combine the environmental sensitivity of the device and the real-time environmental variables to make the prediction results more accurate and reliable. This method can not only help operators to discover potential problems in a timely manner, but also dynamically adjust the equipment operation strategy according to environmental changes, extend the service life of the equipment and improve the stability and safety of the system operation. In addition, the real-time calculation of the failure rate can be used as an important reference for task allocation or maintenance priority sorting, thereby optimizing resource management and system efficiency.

[0033] The resource allocation module includes a feedback optimization unit, which is used to dynamically adjust the resource allocation strategy by comparing the real-time task processing efficiency with the expected efficiency. In this embodiment, the resource allocation module compares the real-time task processing efficiency with the expected efficiency through the feedback optimization unit, and dynamically adjusts the resource allocation strategy based on the comparison result. Specifically, the system first sets the expected efficiency value of the task, which can be determined based on historical data, task type or equipment performance indicators. The feedback optimization unit monitors the processing efficiency of the current task in real time and calculates the difference between the actual efficiency and the expected efficiency. When the difference exceeds the preset threshold, the system automatically adjusts the resource allocation strategy, such as increasing resource supply or adjusting task priority, to optimize task processing performance. Through the feedback optimization mechanism, the system can correct the deviation in resource allocation in time to ensure the maximization of overall operating efficiency. This embodiment realizes the dynamic linkage between task processing efficiency and resource allocation strategy by introducing the feedback optimization unit. The feedback mechanism can quickly respond to the fluctuation of task processing efficiency, thereby optimizing resource allocation in time and avoiding efficiency reduction or resource waste caused by uneven distribution. Compared with the traditional static allocation strategy, this dynamic adjustment mechanism has higher flexibility and adaptability, which can significantly improve the overall performance and resource utilization of the exhibition hall system, while reducing equipment overload or task delays caused by allocation problems.

[0034] The monitoring module can generate a periodic health status report and record the health status change trend of the equipment in detail. In this embodiment, the monitoring module regularly generates a health status report by real-time collection and analysis of the operating status data of the interactive equipment in the exhibition hall. The report records in detail the health status change trend of the equipment, including the initial health status, the state change during operation, the health decay amplitude and the key parameters affecting the health status (such as task load, running time, environmental variables, etc.). The monitoring module generates a health status trend chart by analyzing historical data, and judges whether the equipment has potential risks or needs maintenance based on the trend. The report can also include predictive analysis content to evaluate the change trend of the health status of the equipment in a certain period of time in the future, and provide a scientific basis for the formulation of equipment maintenance plans. This embodiment can provide detailed data support and visual information for the equipment management of the exhibition hall by generating a periodic health status report. The health status change trend recorded in the report can help operators fully understand the equipment operation status, timely discover potential problems and take preventive measures, so as to avoid sudden equipment failures. The health status report not only improves the scientific nature of equipment maintenance, but also provides a data basis for equipment update or optimization, thereby extending the service life of the equipment and improving the operating efficiency and stability of the exhibition hall system.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data collection and automation control system for a smart exhibition hall of a science and technology museum, characterized in that: The system comprises: A load balancing module, which is used to receive data processing requests from multiple interactive systems and allocate computing resources through a load balancing algorithm; The resource allocation module connected to the load balancing module is used to adjust the allocation strategy of computing resources based on historical data and real-time feedback to optimize the system response speed, including dividing the equipment data processing request tasks in the smart exhibition hall into first priority and second priority, defining the ratio p of resources allocated to the first priority tasks and the ratio q of resources allocated to the second priority tasks, and dynamically adjusting the ratio of p and q according to task requirements so that the formula p 2 +2pq+q 2 =1 holds, where p represents the proportion of resources allocated to the first priority task, and q represents the proportion of resources allocated to the second priority task; The monitoring module connected to the resource allocation module is used to monitor the operating status of each interactive device in the exhibition hall in real time and predict faults; The parameter adjustment module connected to the resource allocation module and the monitoring module is used to dynamically adjust the operating parameters of the interactive device according to the monitoring results and prediction data.

2. According to claim 1, the data acquisition and automation control system for the intelligent exhibition hall of a science and technology museum is characterized by: The load balancing module receives data requests from multiple interactive systems and allocates computing resources through a load balancing algorithm including: Calculate the current task processing speed of the equipment in the smart exhibition hall. The specific formula is: ； Among them, v represents the current task processing speed, v max represents the maximum processing speed under ideal conditions, ρ represents the number of tasks assigned to the device per unit time, and ρ max Indicates the number of tasks assigned to the device per unit time at the performance limit of the device; Calculate the amount of tasks completed by each device per unit time. The specific formula is: u=v×ρ; Among them, u represents the amount of tasks completed by the device per unit time, v represents the current task processing speed, and ρ represents the number of tasks assigned to the device per unit time; Then adjust the task allocation strategy, update the number of tasks assigned to the device per unit time in real time, and recalculate the allocation strategy to achieve a balanced distribution of task flows.

3. According to claim 1, the data acquisition and automation control system for the intelligent exhibition hall of a science and technology museum is characterized by: The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction, including: Initialize the health status of the equipment in the smart exhibition hall, record the start time of the equipment, calculate the health status of the equipment in real time, and monitor the fluctuation of the failure rate. The specific formula is: ; Where N(t) represents the degree of change of the health status of the equipment within time t, t represents time, N0 represents the health status of the equipment at the beginning of operation, λ represents the equipment failure rate, and e represents the base of the natural logarithm; Set the threshold T h , when N(t)≤T h When the fault occurs, an early warning is issued to indicate that the equipment is about to fail.

4. According to claim 1, the data acquisition and automation control system for the intelligent exhibition hall of a science and technology museum is characterized by: The parameter adjustment module dynamically adjusts the operating parameters of the interactive device according to the monitoring results and the prediction data, including: Collect the current task load of the equipment in the smart exhibition hall, determine the proportional coefficient based on the hardware performance, and calculate the adjustment rate of the equipment operation parameters. The specific formula is: ; Among them, R represents the adjustment rate of the equipment's operating parameters, a represents the proportional coefficient, M represents the current task load of the equipment, and b represents the efficiency coefficient of the equipment adjustment; According to the calculation results of the rate adjustment of the equipment's operating parameters, adjust the equipment's operating parameters to adapt to the load requirements, record the adjustment results, optimize the values ​​of the efficiency coefficient and proportional coefficient of the equipment adjustment, and improve the adjustment accuracy.

5. The data acquisition and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 2 is characterized by: The load balancing module receives data requests from multiple interactive systems and allocates computing resources through a load balancing algorithm. The calculation formula for the number of tasks ρ allocated to the device per unit time is: ρ=E / F; Among them, ρ represents the number of tasks assigned to the device per unit time, E represents the task request, and F represents the device response time.

6. The data collection and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 2 is characterized by: The load balancing module receives data requests from multiple interactive systems and allocates computing resources through a load balancing algorithm, with the task allocation strategy prioritizing the first priority task.

7. The data collection and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 3 is characterized by: The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs fault prediction. The monitoring module evaluates the degree of change of device performance during operation by calculating the attenuation amplitude of the device health status. The specific calculation formula is: ΔN=N0-N(t); Among them, ΔN represents the attenuation amplitude of the equipment health status, N0 represents the health status of the equipment at the beginning of operation, and N(t) represents the degree of change of the equipment health status within time t.

8. The data collection and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 3 is characterized by: The monitoring module monitors the operating status of each interactive device in the exhibition hall in real time and performs a calculation formula for the equipment failure rate λ in the fault prediction: λ=λ0+k×G; Among them, λ represents the equipment failure rate, λ0 represents the basic failure rate of the equipment, k represents the environmental sensitivity coefficient, and G represents the external environmental variables that affect the equipment failure rate.

9. The data collection and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 1 is characterized by: The resource allocation module includes a feedback optimization unit for dynamically adjusting the resource allocation strategy by comparing the real-time task processing efficiency with the expected efficiency.

10. The data collection and automation control system for the intelligent exhibition hall of a science and technology museum according to claim 1 is characterized by: The monitoring module can generate periodic health status reports, recording in detail the health status change trends of the equipment.

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