An intelligent management system based on land survey data

Abstract

The present invention relates to the field of intelligent management technology, and in particular to an intelligent management system based on land survey data, which collects sample images of land survey data for storage through an acquisition component, monitors the temperature of the hardware storage device area and obtains the resolution of the hardware storage device data sample images through a monitoring module, and stores the monitored historical data through a priori analysis module. Furthermore, a control module determines the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value, and monitors the hardware storage device to determine whether the land survey data transmission efficiency predicted by the sample image characterization parameters meets the predetermined standard, so as to determine whether to enable the thermal compensation device. The present invention takes into account the impact of the temperature difference of the hardware storage device on the land survey data sample image acquisition efficiency during the system operation, and adaptively intervenes in the thermal compensation device, thereby ensuring the land survey data sample image acquisition efficiency of the intelligent system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to an intelligent management system based on land survey data. Background Art

[0002] As land survey data management evolves, traditional manual processing models face dual bottlenecks in efficiency and accuracy. With the widespread adoption of remote sensing mapping, IoT sensors, and drone technology, land survey data is becoming multi-source and heterogeneous, encompassing massive amounts of data, including imagery, soil topography and boundary indicators, and ownership information. Traditional databases struggle to efficiently integrate and dynamically update these data. Furthermore, policies such as national land spatial planning and farmland protection place higher demands on data real-time performance and spatial analysis capabilities. Manual verification suffers from long processing times and high error rates. Furthermore, existing management systems often focus on storing a single data type and lack intelligent management across the entire data collection, processing, and analysis process, making them unable to meet the demands of dynamic natural resource monitoring. The development of artificial intelligence, big data analysis, and GIS technologies has provided technical support for building intelligent management systems capable of automatic data classification, anomaly identification, and trend prediction. Intelligent approaches are urgently needed to address the efficiency, accuracy, and application depth challenges of land survey data management.

[0003] Chinese patent publication number CN214275205U discloses a fully digital field information collection device for land surveys, comprising a base, a housing fixedly connected to one side of the base, a support frame fixedly connected to one side of the housing, a motor fixedly connected to one side of the support frame, a first sleeve sleeved and fixedly connected to the motor output shaft, a first sleeve fixedly connected to one side of the housing, a first turntable sleeved on the outer wall of the first sleeve, a support column rotatably connected to one side of the housing, a second turntable sleeved on the support column, and a belt, both the first and second turntables rotatably connected to the belt, a support plate fixedly connected to one side of the support column, a flip mechanism provided on the support plate, and a camera fixedly connected to one side of the flip mechanism. The camera can rotate to capture images from multiple angles, thereby acquiring image information within a specified range.

[0004] Chinese Patent Publication No. CN118651451B discloses a device for collecting data on intensive agricultural land use surveys, comprising an unmanned aerial vehicle (UAV) comprising a fuselage, a plurality of rotors mounted around the fuselage, and a bracket disposed at the bottom of the fuselage. The device also comprises an information collection cabin and an equipment cabin mounted on the UAV, wherein the cabin houses an information collection module for collecting land intensive information. The device automatically replaces the equipment cabin when the battery of the UAV's operating equipment runs out, thereby improving the UAV's endurance. Furthermore, the device automatically rotates the outer shell of the UAV when the UAV encounters harsh environments, and uses a cleaning block to clean the outer shell, thereby improving the UAV's photography performance in harsh environments.

[0005] However, the prior art still has the following problems:

[0006] The existing technology does not take into account the problem that the long-term operation of cameras and hardware storage devices in different historical periods may cause different heating levels in the hardware storage device areas due to the influence of environmental temperature factors, resulting in performance differences, which in turn affects the efficiency of land survey data transmission. Summary of the Invention

[0007] In order to solve the above problems, the present invention provides an intelligent management system based on land survey data, which overcomes the problem in the prior art that the camera and hardware storage devices are not taken into account in different historical periods. The long-term operation of the camera and the hardware storage devices causes different heating levels in the hardware storage device areas due to the influence of environmental temperature factors, resulting in performance differences, which in turn affects the land survey data transmission efficiency and causes low efficiency in land survey data sample image acquisition.

[0008] To achieve the above objectives, the present invention provides an intelligent management system based on land survey data, comprising:

[0009] An acquisition component comprising a camera for acquiring sample images of land survey data, a hardware storage device connected to the camera for storing the sample images, and a thermal compensation device for changing the temperature of the camera;

[0010] A monitoring module, comprising a plurality of temperature sensors for monitoring the temperature of the hardware storage device and the temperature of the camera, and an image monitoring unit for obtaining the image resolution of data samples of the hardware storage device;

[0011] an a priori analysis module for storing historical data monitored by the monitoring module, for identifying equipment operation risk cycles based on temperature differences of the camera in different historical cycles, and for analyzing equipment operation risk tendency characterization values ​​of the hardware storage device in different equipment operation risk cycles based on the image resolution of the data samples;

[0012] A control module is connected to the acquisition component, the monitoring module and the prior analysis module respectively, and is used to determine the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value within different equipment operation risk periods; and monitor the operating status of the hardware storage device in response to the determination result, including:

[0013] The sample image characterization parameters are determined based on the change in the temperature of the hardware storage device within a predetermined period and the change ratio of the data sample image storage resolution to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standards, and whether to enable the thermal compensation device is determined based on the current actual land survey data sample image resolution.

[0014] Preferably, the a priori analysis module is used to identify the equipment operation risk period based on the temperature difference of the camera in different historical periods, including:

[0015] Calculate the variance of the temperature in the camera area within a single historical period;

[0016] If the temperature variance is greater than a predetermined variance threshold, it is determined to be a risky period for the equipment operation.

[0017] Preferably, the a priori analysis module is used to analyze the device operation risk tendency characterization value of the hardware storage device in different device operation risk cycles based on the data sample image resolution, including:

[0018] Calculating a ratio of a rate of change of a data sample image resolution of a hardware storage device to a rate of change threshold value and determining it as a first historical data feature;

[0019] Calculating a ratio of a transmission rate change amplitude of the hardware storage device to a transmission rate change amplitude threshold and determining it as a second historical data feature;

[0020] The sum of the first historical data feature and the second historical data feature is determined as the equipment operation risk tendency characterization value.

[0021] Preferably, the control module is configured to determine the actual equipment operation risk as a significant risk impact period and a weak risk impact period based on the equipment operation risk tendency characterization values ​​in different equipment operation risk periods, including:

[0022] If the equipment operation risk tendency characterization value is greater than or equal to the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a significant risk impact period;

[0023] If the equipment operation risk tendency characterization value is less than the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a weak risk impact period.

[0024] Preferably, the control module monitors the operating status of the hardware storage device in response to the determination result, including:

[0025] If the result of the determination is that the condition of the significant risk impact period exists, the sample image characterization parameters are determined based on the amount of change in the temperature of the hardware storage device area and the change ratio of the data sample image storage resolution within the predetermined period to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard, and whether to enable the thermal compensation device is determined based on the current actual land survey data sample image resolution;

[0026] If the result of the determination is that the risk affects the cycle condition slightly, the thermal compensation device will not be activated.

[0027] Preferably, the control module is used to determine the sample image characterization parameters based on the change in temperature of the hardware storage device and the change ratio of the data sample image storage resolution within a predetermined period, including:

[0028] The ratio of the change amount of the temperature of the hardware storage device to the temperature change threshold is calculated and determined as the first data sample image resolution impact factor;

[0029] The ratio of the change amount of the data sample image storage resolution of the hardware storage device to the data sample image storage resolution change threshold is calculated and determined as the second data sample image resolution influencing factor;

[0030] The sum of the first data sample image resolution impact factor and the second data sample image resolution impact factor is calculated and determined as the sample image representation parameter.

[0031] Preferably, the control module is used to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard based on the sample image characterization parameters, including:

[0032] If the sample image representation parameter is less than or equal to the preset sample image representation parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device meets the predetermined standard;

[0033] If the sample image characterization parameter is greater than the preset sample image characterization parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device does not meet the predetermined standard.

[0034] Preferably, the control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution when it is predicted that the land survey data transmission efficiency of the hardware storage device does not meet a predetermined standard.

[0035] Preferably, the control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution, including:

[0036] If the predetermined data sample image resolution threshold is less than or equal to the current data sample image resolution, determining to enable the thermal compensation device;

[0037] If the predetermined data sample image resolution threshold is greater than the current data sample image resolution, it is determined that the thermal compensation device is not enabled.

[0038] Preferably, it further comprises a display module connected to the monitoring module for displaying the data monitored by the monitoring module.

[0039] Compared with the prior art, the present invention provides an intelligent management system based on land survey data, including an acquisition component, a monitoring module, a priori analysis module, and a control module. The acquisition component collects sample images of land survey data for storage and changes the thermal compensation status of the temperature of the hardware storage device area according to actual conditions. The monitoring module monitors the temperature of the hardware storage device area and obtains the resolution of the hardware storage device data sample image. The priori analysis module stores the historical data monitored by the monitoring module. Moreover, the control module determines the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value within different equipment operation risk cycles, monitors the hardware storage device, determines the sample image characterization parameter to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard, so as to determine whether to enable the thermal compensation device. Considering the impact of the temperature difference of the hardware storage device on the land survey data sample image acquisition efficiency during the operation of the intelligent system, the thermal compensation device is adaptively intervened, thereby ensuring the land survey data sample image acquisition efficiency and power generation efficiency of the intelligent system.

[0040] In particular, the present invention can accurately obtain the patterns of how hardware storage devices are affected by temperature in different time periods by identifying the equipment operation risk cycles, and can accurately determine the equipment operation risk cycles of historical periods by calculating the variance of the temperatures of the hardware storage device areas in each historical period. Moreover, the changes in the resolution of the data sample images can accurately analyze the equipment operation risk tendency characterization values ​​of the hardware storage devices in different equipment operation risk cycles, thereby improving the efficiency of the hardware storage devices.

[0041] In particular, the present invention can determine the sample image characterization parameters through the change in camera temperature and the change ratio of the data sample image storage resolution, thereby improving the accuracy of the land survey data transmission efficiency of the hardware storage device. The land survey data transmission efficiency of the hardware storage device can be predicted through the sample image characterization parameters, and whether the performance of the hardware storage device will decline can be predicted in advance, reducing the impact of the hardware storage device during the device operation risk cycle on the storage resolution causing a decline, and improving the stability of the hardware storage device and the land survey data sample image acquisition efficiency.

[0042] In particular, the present invention can determine the significant risk impact period and the weak risk impact period through the equipment operation risk tendency characterization value within different equipment operation risk periods, thereby improving energy utilization efficiency. Moreover, by determining whether the thermal compensation device of the hardware storage device is enabled, the energy utilization efficiency is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a structural block diagram of an intelligent management system based on land survey data according to an embodiment of the present invention;

[0044] Figure 2 This is a logic decision diagram for identifying a device operation risk cycle according to an embodiment of the present invention;

[0045] Figure 3 A logical decision diagram for analyzing a device operation risk tendency characterization value of a hardware storage device according to an embodiment of the present invention;

[0046] Figure 4 This is a logical decision diagram for determining a significant risk impact period and a weak risk impact period according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the term "connection" should be understood in a broad sense. For example, it can mean a fixed connection, a detachable connection, or an integral connection; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0050] See also Figure 1 As shown in FIG, which is a structural block diagram of an intelligent management system based on land survey data according to an embodiment of the present invention, the present invention provides an intelligent management system based on land survey data, which is characterized by comprising:

[0051] An acquisition component comprising a camera for acquiring sample images of land survey data, a hardware storage device connected to the camera for storing the sample images, and a thermal compensation device for changing the temperature of the camera;

[0052] A monitoring module, comprising a plurality of temperature sensors for monitoring the temperature of the hardware storage device and the temperature of the camera, and an image monitoring unit for obtaining the image resolution of data samples of the hardware storage device;

[0053] an a priori analysis module for storing historical data monitored by the monitoring module, for identifying equipment operation risk cycles based on temperature differences of the camera in different historical cycles, and for analyzing equipment operation risk tendency characterization values ​​of the hardware storage device in different equipment operation risk cycles based on the image resolution of the data samples;

[0054] A control module is connected to the acquisition component, the monitoring module and the prior analysis module respectively, and is used to determine the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value within different equipment operation risk periods; and monitor the operating status of the hardware storage device in response to the determination result, including:

[0055] The sample image characterization parameters are determined based on the change in the temperature of the hardware storage device within a predetermined period and the change ratio of the data sample image storage resolution to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standards, and whether to enable the thermal compensation device is determined based on the current actual land survey data sample image resolution.

[0056] Specifically, the system includes an acquisition component, a monitoring module, a priori analysis module, and a control module. The acquisition component collects sample images of land survey data for storage and changes the thermal compensation status of the temperature of the hardware storage device area according to actual conditions. The monitoring module monitors the temperature of the hardware storage device area and obtains the resolution of the hardware storage device data sample image. The priori analysis module stores the historical data monitored by the monitoring module. Moreover, the control module determines the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value within different equipment operation risk cycles, monitors the hardware storage device, determines the sample image characterization parameter to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard, so as to determine whether to enable the thermal compensation device, consider the impact of the temperature difference of the hardware storage device on the land survey data sample image acquisition efficiency during the operation of the intelligent system, and adaptively intervene in the thermal compensation device to ensure the land survey data sample image acquisition efficiency of the intelligent system.

[0057] Specifically, the present invention is applied to the intelligent management of land survey data collection. Common collection components include cameras and hardware storage devices. The hardware storage device is usually installed on the back of the camera. Due to sunlight exposure, there are differences on both sides of the hardware storage device. In addition, in some cases, there may also be certain temperature differences in the region.

[0058] Specifically, there is no specific limitation on the structure of the monitoring module, the priori analysis module, the control module itself and each unit therein, and they can be composed of logical components, which include field programmable processors, computers or microprocessor modules in computers.

[0059] Specifically, there is no limitation on the form of the hardware storage device, and it can be in the form of a solid-state drive SSD, a flash memory storage USB flash drive, an SD card, a CF card, or other forms, which will not be repeated here.

[0060] Specifically, the form of the thermal compensation device is not limited. It can be liquid-cooled or air-cooled. For example, a small cooling fan is set on the hardware storage device to dissipate heat for the hardware storage device. Other forms can also be used, which will not be repeated here.

[0061] Specifically, there is no specific limitation on the location of the temperature sensor. The temperature sensor can be located in areas on both sides of the hardware storage device, which will not be described in detail here.

[0062] Specifically, the data sample image detection unit may be a logic component for acquiring information, for example, it may access an image management system to acquire relevant resolution of the sample image, which will not be elaborated herein.

[0063] See also Figure 2The above is a logic decision diagram for identifying the equipment operation risk cycle according to an embodiment of the present invention. The prior analysis module of the present invention is used to identify the equipment operation risk cycle based on the temperature difference of the camera in different historical periods, including:

[0064] Calculate the variance of the temperature in the camera area within a single historical period;

[0065] If the temperature variance is less than or equal to the predetermined variance threshold, it is determined that the equipment is operating in a standard cycle;

[0066] If the temperature variance is greater than a predetermined variance threshold, it is determined to be a risky period for the equipment operation.

[0067] Specifically, the variance threshold is determined based on the average variance of the temperatures obtained in several historical periods and is set between 1.15 and 1.25 times the average variance.

[0068] Specifically, in order to reflect the monitorability of temperature changes and their impact on the equipment, the historical period is selected in the interval [1h, 2h].

[0069] See also Figure 3 As shown, it is a logical decision diagram for analyzing the device operation risk tendency characterization value of the hardware storage device according to an embodiment of the present invention. The priori analysis module of the present invention is used to analyze the device operation risk tendency characterization value of the hardware storage device in different device operation risk cycles based on the data sample image resolution, including:

[0070] Calculating a ratio of a rate of change of a data sample image resolution of a hardware storage device to a rate of change threshold value and determining it as a first historical data feature;

[0071] Calculating a ratio of a transmission rate change amplitude of the hardware storage device to a transmission rate change amplitude threshold and determining it as a second historical data feature;

[0072] The sum of the first historical data feature and the second historical data feature is determined as the equipment operation risk tendency characterization value.

[0073] Specifically, the rate of change of the storage resolution of the data sample image reflects the change in the efficiency of the hardware storage device in storing data sample images under different device operation risk cycles. The change rate threshold is a reference value used to measure whether the degree of such change is significant. In implementation, it is set to the average change rate of the data sample image in several historical periods. By calculating the ratio, the change rate can be standardized, which is convenient for comparison and comprehensive analysis with other features.

[0074] Specifically, the ratio of the transmission rate change rate to the transmission rate change threshold is similar to the transmission rate change. The change in transmission will also be affected by temperature. This ratio can reflect the degree of influence of temperature on the change in transmission rate. The transmission rate change threshold is set to the average value of the transmission rate change rate in several historical periods in implementation. The sum of these two ratios is used as the first historical data feature, and the changes in the two important data sample image resolutions of storage resolution and transmission under different equipment operation risk cycles are comprehensively considered.

[0075] Specifically, the first and second historical data features are added together to generate a device operation risk propensity value. This value integrates the temperature impact of multiple data sample image resolutions, such as changes in storage resolution and transmission rate, across different device operation risk cycles. This value comprehensively reflects the temperature impact propensity of hardware storage devices within a specific device operation risk cycle.

[0076] Specifically, by analyzing multiple data sample image resolutions, we can more accurately assess the impact of temperature on hardware storage devices. Different data sample image resolutions reflect device performance changes from different perspectives. Comprehensively considering these resolutions can reduce the limitations of single-resolution assessments and provide more comprehensive and accurate temperature impact assessments.

[0077] See also Figure 4 As shown, it is a logical determination diagram for determining a significant risk impact period and a weak risk impact period in an embodiment of the present invention. The control module of the present invention is used to determine a significant risk impact period and a weak risk impact period based on the equipment operation risk tendency characterization value within different equipment operation risk periods, including:

[0078] If the equipment operation risk tendency characterization value is greater than or equal to the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a significant risk impact period;

[0079] If the equipment operation risk tendency characterization value is less than the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a weak risk impact period.

[0080] Specifically, the preset equipment operation risk tendency characterization value serves as a key judgment criterion, playing the role of determining the significant risk impact period and the weak risk impact period. In implementation, the preset equipment operation risk tendency characterization value is selected within the range of [2.15,2.35].

[0081] Specifically, when the device operation risk tendency characterization value is greater than or equal to the preset device operation risk tendency characterization value, it is determined to be a significant risk impact period. This means that during this period, the temperature has a significant impact on the hardware storage device, which may cause significant changes in device performance. Preferably, regional differences in device properties and regional device capacity lead to reduced transmission efficiency, or rapid temperature changes may cause stress changes within the device, affecting the device's lifespan and safety.

[0082] Specifically, if the device's operational risk propensity value is less than the preset value, it is considered a period of minimal risk impact. During this period, temperature has a relatively small impact on the hardware storage device, and device performance changes are relatively gradual. During this period, the device can operate in a relatively stable state, with performance indicators such as transmission efficiency and lifespan remaining relatively stable.

[0083] Specifically, the control module is further configured to disable the thermal compensation device when the determination result is that the risk impact period is slight, including:

[0084] If the result of the determination is that the condition of the significant risk impact period exists, the sample image characterization parameters are determined based on the amount of change in the temperature of the hardware storage device area and the change ratio of the data sample image storage resolution within the predetermined period to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard, and whether to enable the thermal compensation device is determined based on the current actual land survey data sample image resolution;

[0085] If the result of the determination is that the risk affects the cycle condition slightly, the thermal compensation device will not be activated.

[0086] Specifically, during periods of significant risk impact, temperature significantly impacts hardware storage devices. By monitoring changes in camera temperature, we can understand the extent of the impact of external ambient temperature changes on the intelligent system. Furthermore, the percentage change in the storage resolution of data sample images reflects the performance changes of the hardware storage devices under different temperature conditions. Combining these two resolutions to determine sample image characterization parameters allows for a more comprehensive assessment of the impact of temperature on the entire intelligent energy storage system.

[0087] Specifically, a sharp rise in camera temperature can cause changes in the output resolution and transmission rate of smart devices, which in turn affects the transmission efficiency of hardware storage devices. By analyzing the temperature change and the change ratio between the storage resolution of data sample images, it is possible to predict whether the hardware storage device's land survey data transmission efficiency meets predetermined standards under such drastic temperature fluctuations.

[0088] Specifically, during periods of significant risk impact, the costs and benefits of using thermal compensation devices need to be considered. The predetermined data sample image resolution threshold is an important consideration; if the power consumption associated with enabling thermal compensation devices is excessive, it could reduce overall system efficiency. Furthermore, the current data sample image resolution needs to be considered. If the data sample image resolution is high, enabling thermal compensation devices may be necessary to reduce data sample image loss due to temperature effects.

[0089] Specifically, if the predetermined data sample image resolution threshold is high, but the current data sample image resolution is relatively low, the thermal compensation device may not be activated. Instead, other measures may be taken, such as adjusting the camera angle or reducing the operating power of the hardware storage device, to reduce the impact of temperature on the system. Conversely, if the data sample image resolution is high and the predetermined data sample image resolution threshold is within an acceptable range, the thermal compensation device may be activated to improve system stability and reliability.

[0090] Specifically, the control module is used to determine the sample image characterization parameters based on the change in temperature of the hardware storage device and the change ratio of the data sample image storage resolution within a predetermined period, including:

[0091] The ratio of the change amount of the temperature of the hardware storage device to the temperature change threshold is calculated and determined as the first data sample image resolution impact factor;

[0092] The ratio of the change amount of the data sample image storage resolution of the hardware storage device to the data sample image storage resolution change threshold is calculated and determined as the second data sample image resolution influencing factor;

[0093] The sum of the first data sample image resolution impact factor and the second data sample image resolution impact factor is calculated and determined as the sample image representation parameter.

[0094] To reflect temperature differences, the temperature change threshold for hardware storage devices is set based on the average temperature value of the hardware devices in the historical period, and is set between 0.35 times and 0.45 times the average temperature value.

[0095] Specifically, the data sample image storage resolution change threshold is obtained in advance, and is set based on the average data sample image storage resolution of the hardware storage device in the historical period, and is set to between 0.55 times and 0.85 times the average data sample image storage resolution.

[0096] Specifically, the change in the data sample image storage resolution of the hardware storage device reflects the performance changes of the hardware storage device under different temperature conditions. Preferably, when the temperature difference is large, the data sample image storage resolution decreases. The larger the change, the more significant the impact on the performance of the hardware storage device.

[0097] Specifically, the sample image characterization parameter is obtained by adding the first data sample image resolution influencing factor and the second data sample image resolution influencing factor. This resolution comprehensively considers the impact of changes in camera temperature and changes in the data sample image storage resolution of the hardware storage device on the data sample image resolution of the entire system. Preferably, if both eigenvalues ​​are large, it indicates that changes in camera temperature and changes in hardware storage device performance have a significant impact on the data sample image resolution, and the sample image characterization parameter will also be large. If both eigenvalues ​​are small, it indicates that changes in temperature and performance have a small impact on the data sample image resolution, and the sample image characterization parameter will also be small.

[0098] Specifically, the control module is used to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard based on the sample image representation parameters, including:

[0099] If the sample image representation parameter is less than or equal to the preset sample image representation parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device meets the predetermined standard;

[0100] If the sample image characterization parameter is greater than the preset sample image characterization parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device does not meet the predetermined standard.

[0101] Specifically, the preset sample image characterization parameters, which serve as key reference values ​​for determining whether the land survey data transmission efficiency of the hardware storage device meets predetermined standards, are determined based on a combination of factors, including the hardware storage device's design performance, historical operating data, and the system's requirements for land survey data transmission efficiency. Preferably, a single value that can be used to determine whether land survey data transmission efficiency meets standards is determined by conducting extensive analysis of the resolution of data sample images from the same type of hardware storage device under different operating conditions.

[0102] Specifically, when the sample image characterization parameter is less than or equal to the preset sample image characterization parameter, the land survey data transmission efficiency of the hardware storage device is predicted to meet the predetermined standard. This indicates that under the current operating conditions, the data sample image resolution, as reflected by the camera temperature change and the hardware storage device's data sample image storage resolution change, is within an acceptable range, and the hardware storage device is able to store energy according to the expected performance requirements. Preferably, the camera temperature change is relatively small, with no significant impact on the hardware storage device. At the same time, the hardware storage device's data sample image storage resolution is maintained at a high level, meeting the system's requirements for energy storage capacity and efficiency.

[0103] Specifically, if the sample image representation parameter is greater than the preset sample image representation parameter, the land survey data transmission efficiency of the hardware storage device is predicted to not meet the predetermined standard. This indicates that the current data sample image resolution has changed beyond the acceptable range. This may be due to excessively high or drastic changes in camera temperature, which has significantly affected the performance of the hardware storage device and reduced the data sample image storage resolution, thus failing to meet the predetermined land survey data transmission efficiency requirements. This has increased energy loss during operation, and reduced data sample image resolution and efficiency.

[0104] Specifically, the control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution when predicting that the land survey data transmission efficiency of the hardware storage device does not meet a predetermined standard.

[0105] Specifically, in an intelligent management system, when the predicted land survey data transmission efficiency of a hardware storage device does not meet predetermined standards, measures are considered to improve the situation. A thermal compensation device is a possible solution, but it also consumes power. Therefore, a comprehensive evaluation of the predetermined data sample image resolution threshold and the current data sample image resolution is required to determine whether enabling the thermal compensation device will improve overall system performance.

[0106] Specifically, the control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution, including:

[0107] If the predetermined data sample image resolution threshold is less than or equal to the current data sample image resolution, determining to enable the thermal compensation device;

[0108] If the predetermined data sample image resolution threshold is greater than the current data sample image resolution, it is determined that the thermal compensation device is not enabled.

[0109] Specifically, it also includes a display module, which is connected to the monitoring module and is used to display the data monitored by the monitoring module.

[0110] Specifically, the display module is connected to the monitoring module and can display the data monitored by the monitoring module in real time. This is crucial for the operation and management of intelligent control systems. Preferably, the display module allows users to keep track of key parameters such as camera temperature, image resolution of data samples from hardware storage devices, and temperature. In practical applications, operators can intuitively see the current status of the system, detect abnormalities promptly, and take appropriate measures.

[0111] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent management system based on land survey data, characterized in that: include: An acquisition component comprising a camera for acquiring sample images of land survey data, a hardware storage device connected to the camera for storing the sample images, and a thermal compensation device for changing the temperature of the camera; A monitoring module, comprising a plurality of temperature sensors for monitoring the temperature of the hardware storage device and the temperature of the camera, and an image monitoring unit for obtaining the image resolution of data samples of the hardware storage device; an a priori analysis module for storing historical data monitored by the monitoring module, for identifying equipment operation risk cycles based on temperature differences of the camera in different historical cycles, and for analyzing equipment operation risk tendency characterization values ​​of the hardware storage device in different equipment operation risk cycles based on the image resolution of the data samples; The equipment operation risk tendency characterization value includes: Calculating a ratio of a rate of change of a data sample image resolution of a hardware storage device to a rate of change threshold value and determining it as a first historical data feature; Calculating a ratio of a transmission rate change amplitude of the hardware storage device to a transmission rate change amplitude threshold and determining it as a second historical data feature; Determining the sum of the first historical data feature and the second historical data feature as the equipment operation risk tendency characterization value; A control module is connected to the acquisition component, the monitoring module and the prior analysis module respectively, and is used to determine the significant risk impact period and the weak risk impact period based on the equipment operation risk tendency characterization value corresponding to different equipment operation risk periods; and monitor the operating status of the hardware storage device in response to the determination result, including: Determining a sample image characterization parameter based on a change in the temperature of the hardware storage device and a change ratio of the data sample image storage resolution within a predetermined period to predict whether the land survey data transmission efficiency of the hardware storage device meets a predetermined standard, and determining whether to enable the thermal compensation device based on the current actual land survey data sample image resolution; The sample image characterization parameters include: The ratio of the change amount of the temperature of the hardware storage device to the temperature change threshold is calculated and determined as the first data sample image resolution impact factor; The ratio of the change amount of the data sample image storage resolution of the hardware storage device to the data sample image storage resolution change threshold is calculated and determined as the second data sample image resolution influencing factor; The sum of the first data sample image resolution impact factor and the second data sample image resolution impact factor is calculated and determined as the sample image representation parameter.

2. The intelligent management system based on land survey data according to claim 1 is characterized in that: The priori analysis module is used to identify the equipment operation risk period based on the temperature difference of the camera in different historical periods, including: Calculate the variance of the temperature in the camera area within a single historical period; If the temperature variance is greater than a predetermined variance threshold, it is determined to be a risky period for the equipment operation.

3. The intelligent management system based on land survey data according to claim 1 is characterized in that: The control module is used to determine the significant risk impact period and the weak risk impact period of the actual equipment operation risk based on the equipment operation risk tendency characterization values ​​corresponding to different equipment operation risk periods, including: If the equipment operation risk tendency characterization value is greater than or equal to the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a significant risk impact period; If the equipment operation risk tendency characterization value is less than the preset equipment operation risk tendency characterization value, the control module determines the actual equipment operation risk as a weak risk impact period.

4. The intelligent management system based on land survey data according to claim 1 is characterized in that: The control module monitors the operating status of the hardware storage device in response to the determination result, including: If the result of the determination is that the condition of the significant risk impact period exists, the sample image characterization parameters are determined based on the amount of change in the temperature of the hardware storage device area and the change ratio of the data sample image storage resolution within the predetermined period to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard, and whether to enable the thermal compensation device is determined based on the current actual land survey data sample image resolution; If the result of the determination is that the risk affects the cycle condition slightly, the thermal compensation device will not be activated.

5. The intelligent management system based on land survey data according to claim 4 is characterized in that: The control module is used to predict whether the land survey data transmission efficiency of the hardware storage device meets the predetermined standard based on the sample image representation parameters, including: If the sample image representation parameter is less than or equal to the preset sample image representation parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device meets the predetermined standard; If the sample image characterization parameter is greater than the preset sample image characterization parameter, it is predicted that the land survey data transmission efficiency of the hardware storage device does not meet the predetermined standard.

6. The intelligent management system based on land survey data according to claim 5 is characterized in that: The control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution when predicting that the land survey data transmission efficiency of the hardware storage device does not meet a predetermined standard.

7. The intelligent management system based on land survey data according to claim 1 is characterized in that: The control module is used to determine whether to enable the thermal compensation device based on a predetermined data sample image resolution threshold and a current data sample image resolution, including: If the predetermined data sample image resolution threshold is less than or equal to the current data sample image resolution, determining to enable the thermal compensation device; If the predetermined data sample image resolution threshold is greater than the current data sample image resolution, it is determined that the thermal compensation device is not enabled.

8. The intelligent management system based on land survey data according to claim 1 is characterized in that: It also includes a display module, which is connected to the monitoring module and is used to display the data monitored by the monitoring module.

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