Intelligent management method for geographic information surveying and mapping data

Abstract

The invention discloses an intelligent management method for geographic information surveying and mapping data, and particularly relates to the technical field of geographic information surveying and mapping data management. The deployment opportunity of a mobile data recovery center is comprehensively analyzed through flood influence range information after a flood disaster occurs and damage degree information of a local data center; the deployment opportunity of the mobile data recovery center is determined, resource waste caused by blind deployment is avoided, it is guaranteed that the mobile data recovery center is put into use at the most appropriate opportunity after flood disasters occur, and the deployment opportunity of the mobile data recovery center is determined by obtaining regional network communication information of a deployment region and importance degree information of data recovery. The instant starting time of data recovery of the mobile data recovery center is comprehensively analyzed, the starting time of recovery work is flexibly determined according to actual conditions, adverse effects caused by too early or too late starting are avoided, the integrity and availability of geographic information surveying and mapping data are guaranteed, and the data recovery efficiency is improved. And the fault tolerance of geographic information surveying and mapping data management under flood disasters is improved.

Description

Technical Field

[0001] The present invention relates to the field of geographic information surveying and mapping data management technology, and more specifically, to a geographic information surveying and mapping data intelligent management method. Background Art

[0002] The intelligent management method of geographic information surveying and mapping data is a method of managing and processing geographic information data using artificial intelligence technology. With the development of geographic information technology and the continuous expansion of application scenarios, the scale and complexity of geographic information surveying and mapping data are increasing, and traditional management methods can no longer meet the requirements for data processing efficiency and quality. Therefore, with the help of artificial intelligence technology and intelligent data management methods, large-scale geographic information data can be processed more efficiently, and the utilization value and management level of data can be improved.

[0003] Disaster recovery capability is a crucial aspect in geographic information surveying and mapping data management, especially in the face of natural disasters, ensuring data integrity and availability is the key. In the event of a flood, local data centers storing geographic information surveying and mapping data may be flooded, damaged, or have data lost. Mobile data recovery centers are widely used as a solution that can respond quickly and flexibly to data recovery when a flood occurs. However, as an uncontrollable natural disaster, the scope of post-disaster impact of floods is unpredictable. Existing geographic information surveying and mapping data management methods need to be further optimized for the deployment timing of mobile data recovery centers and whether they need to be started immediately, in order to ensure a more intelligent and flexible determination of the deployment timing and immediate activation timing of mobile data recovery centers, improve data recovery efficiency after floods, and ensure the integrity and availability of geographic information surveying and mapping data.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for intelligent management of geographic information surveying and mapping data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for intelligent management of geographic information surveying and mapping data comprises the following steps:

[0008] Step S1, obtaining flood impact range information and damage degree information of the local data center after the flood disaster occurs, performing a comprehensive analysis on the deployment timing of the mobile data recovery center according to the flood impact range information and the damage degree information of the local data center, and obtaining a deployment timing index;

[0009] Step S2, comparing the deployment timing index with the deployment timing index threshold to determine whether a mobile data recovery center needs to be deployed, and when a mobile data recovery center needs to be deployed, performing a site selection operation on the mobile data recovery center and deploying the mobile data recovery center in the selected deployment area;

[0010] Step S3, obtaining regional network communication information and data recovery importance information of the deployment area, comprehensively analyzing the immediate activation timing of the mobile data recovery center for data recovery, and obtaining an activation timing index;

[0011] Step S4, comparing the activation timing index with the activation timing index threshold, and determining the immediate activation timing of the mobile data recovery center for data recovery.

[0012] In a preferred embodiment, the flood impact range information includes the water flow anomaly coefficient, and the damage degree information of the local data center includes the power supply damage fluctuation coefficient and the internal humidity rise rate;

[0013] The water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are marked as sly, gds, and nbs, respectively.

[0014] In a preferred embodiment, the logic for obtaining the water flow anomaly coefficient is as follows:

[0015] Set the period for obtaining flood impact range information;

[0016] At the start and end of the acquisition cycle, the water flow, water flow velocity, and water flow direction deviation values ​​within a preset area around the local data center after the flood occurs are respectively obtained;

[0017] The water flow anomaly coefficient is calculated based on the water flow rate, water flow velocity and water flow direction deviation values ​​at the start and end of the acquisition cycle. The expression is as follows Where q flood Indicates the water flow at the end of the acquisition cycle, q normal Indicates the water flow at the start of the acquisition cycle, v flood Indicates the water velocity at the end of the acquisition cycle, v normal Indicates the water flow velocity at the start of the acquisition cycle, d flood Indicates the water flow direction deviation value at the end of the acquisition cycle, d normal Indicates the deviation value of the water flow direction at the start time of the acquisition cycle;

[0018] The logic for obtaining the power supply damage fluctuation coefficient is as follows:

[0019] Set the period for obtaining damage information of the local data center;

[0020] The total power outage time, standard deviation of power supply voltage, power supply restoration time, and backup power supply operation time of the local data center are obtained during the acquisition cycle;

[0021] The power supply damage fluctuation coefficient is calculated based on the total power outage time, the standard deviation of the power supply voltage, the power supply recovery time, and the backup power supply operation time. The expression is as follows In the formula, gdz represents the total power interruption time, T represents the acquisition period, gdb represents the standard deviation of the power supply voltage, and gdb z Indicates the standard deviation of the preset power supply voltage, ghs indicates the power supply recovery time, and byd indicates the backup power supply operation time;

[0022] The calculation expression of the internal humidity rise rate is as follows Where T represents the acquisition period, sd1 represents the humidity at the start of the acquisition period, and sd2 represents the humidity at the end of the acquisition period.

[0023] In a preferred embodiment, the water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are normalized, and a deployment timing prediction model is constructed based on the normalized water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate to generate a deployment timing index sjz. The model is based on the following formula: Wherein a1, a2, and a3 represent the preset proportional coefficients of the water flow anomaly coefficient, the power supply damage fluctuation coefficient, and the internal humidity rise rate, respectively, and a1, a2, and a3 are all greater than 0.

[0024] In a preferred implementation, the deployment opportunity index is compared with the deployment opportunity index threshold to determine whether a mobile data recovery center needs to be deployed, as follows:

[0025] If the deployment timing index is greater than the deployment timing index threshold, a mobile data recovery center needs to be deployed;

[0026] If the deployment timing index is less than or equal to the deployment timing index threshold, there is no need to deploy the mobile data recovery center immediately.

[0027] In a preferred embodiment, the regional network communication information includes the regional network communication fluctuation coefficient, and the importance information of data recovery includes the importance ratio of data recovery;

[0028] The regional network communication fluctuation coefficient and data recovery importance ratio are marked as qyw and szy respectively.

[0029] In a preferred embodiment, the logic for obtaining the regional network communication fluctuation coefficient is as follows:

[0030] Obtain network communication data in the deployment area within the K time period, including but not limited to packet delay and packet loss rate;

[0031] Calculate the average packet delay at different times The expression is as follows Where L i represents the packet delay at the i-th moment;

[0032] Calculate the average packet loss rate at different times The expression is as follows In the formula represents the packet loss rate at the i-th moment;

[0033] Calculate the regional network communication fluctuation coefficient, the expression is as follows

[0034] In a preferred embodiment, the calculation expression of the data recovery importance ratio is as follows: Where F1 represents the amount of online data access to the damaged local data center within the K time period, and F2 represents the amount of data storage in the damaged local data center within the K time period.

[0035] In a preferred embodiment, the regional network communication fluctuation coefficient and the data recovery importance ratio are normalized, and an activation timing prediction model is constructed based on the normalized regional network communication fluctuation coefficient and the data recovery importance ratio to generate an activation timing index qsz. The formula of the model is as follows: Where b1 and b2 represent the regional network communication fluctuation coefficient and the preset proportional coefficient of the importance of data recovery, respectively, and both b1 and b2 are greater than 0.

[0036] In a preferred embodiment, the activation timing index is compared with the activation timing index threshold to determine the immediate activation timing of the mobile data recovery center for data recovery, as follows:

[0037] If the activation timing index is greater than the activation timing index threshold, the mobile data recovery center needs to be immediately activated to recover the data in the damaged local data center;

[0038] If the activation timing index is less than or equal to the activation timing index threshold, there is no need to immediately activate the mobile data recovery center to recover the data in the damaged local data center.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention uses the flood impact range information and the damage degree information of the local data center after the flood disaster occurs to comprehensively analyze the deployment timing of the mobile data recovery center according to the flood impact range information and the damage degree information of the local data center, and compares the obtained deployment timing index with the deployment timing index threshold to determine the deployment timing of the mobile data recovery center, avoid the waste of resources caused by blind deployment, make the deployment timing more accurate and efficient, and ensure that the mobile data recovery center is put into use at the most appropriate time after the flood disaster occurs, and obtain the regional network communication information and data recovery importance information of the deployment area to comprehensively analyze the immediate activation timing of the mobile data recovery center for data recovery, and compare the obtained activation timing index with the activation timing index threshold to flexibly determine the startup time of the recovery work according to the actual situation, avoid the adverse effects caused by too early or too late startup, ensure the integrity and availability of geographic information surveying and mapping data, and increase the fault tolerance of geographic information surveying and mapping data management under flood disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0042] Figure 1 Schematic diagram of the structure of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] Example

[0045] Figure 1 The present invention provides a method for intelligent management of geographic information surveying and mapping data, comprising the following steps:

[0046] Step S1, obtaining flood impact range information and damage degree information of the local data center after the flood disaster occurs, performing a comprehensive analysis on the deployment timing of the mobile data recovery center according to the flood impact range information and the damage degree information of the local data center, and obtaining a deployment timing index;

[0047] The flood impact range information includes the water flow anomaly coefficient, and the damage degree information of the local data center includes the power supply damage fluctuation coefficient and the internal humidity rise rate;

[0048] The water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are marked as sly, gds, and nbs, respectively;

[0049] The water flow anomaly coefficient is an important indicator for measuring the impact of floods on local data centers. It is used to evaluate the rising rate of potential threats of floods to local data centers and to quantify the degree of abnormality in water flow, water flow velocity and water direction within the flood-affected area of ​​local data centers. The larger the water flow anomaly coefficient, the greater the impact of the flood on the local data center, and the greater the probability of deploying a mobile data recovery center.

[0050] The larger the water flow anomaly coefficient, the greater the impact of the flood on the local data center. Specifically, a higher water flow anomaly coefficient reflects abnormal changes and potential risks in the following aspects:

[0051] Significant increase in water flow and flood volume: Higher water flow may lead to more serious flooding risks and increase the possibility of flooding of facilities inside local data centers;

[0052] Increased pressure on the drainage system: The surge in water flow may exceed the processing capacity of the existing drainage system, resulting in poor drainage or backflow;

[0053] The significant increase in water flow speed and increased destructive power: The increase in water flow speed means that the water has a greater impact force and may cause greater damage to the physical structure and equipment of the local data center.

[0054] Erosion and scouring: High-velocity water flow may exacerbate foundation erosion, weaken the stability of local data center buildings, and even cause structural damage;

[0055] Significant changes in water flow direction, unexpected paths: Changes in water flow direction may cause floods to flow to local data centers in unexpected paths, breaking through existing flood control measures.

[0056] Failure of protective measures: protective measures originally designed for a specific water flow direction (such as retaining walls, dams, etc.) may not be able to effectively prevent water intrusion from a new direction;

[0057] Therefore, by obtaining the flood impact range information, the abnormal degree of water flow, water flow speed and water direction in the impact range after the flood occurs is analyzed, and the water flow abnormality coefficient is obtained to measure the impact of the flood on the local data center;

[0058] The logic for obtaining the water flow anomaly coefficient is as follows:

[0059] Set the period for obtaining flood impact range information;

[0060] At the start and end of the acquisition cycle, the water flow, water flow velocity, and water flow direction deviation values ​​within a preset area around the local data center after the flood occurs are respectively obtained;

[0061] It should be noted that the water flow and water flow velocity can be obtained in real time by installing flow meters and flow velocity meters within the preset area around the local data center. The water flow direction deviation value can be obtained by remote sensing drone technology to obtain the geographical coordinates of the flood flow direction, and the water flow direction deviation value can be calculated by Euclidean distance;

[0062] Specifically, the water flow direction deviation value is calculated by Euclidean distance, and the expression is as follows Where (x1, y1) represents the geographical coordinates of the flood flow direction, and (x2, y2) represents the geographical coordinates of the expected path;

[0063] The water flow anomaly coefficient is calculated based on the water flow rate, water flow velocity and water flow direction deviation values ​​at the start and end of the acquisition cycle. The expression is as follows Where q flood Indicates the water flow at the end of the acquisition cycle, q normal Indicates the water flow at the start of the acquisition cycle, v flood Indicates the water velocity at the end of the acquisition cycle, v normal Indicates the water flow velocity at the start of the acquisition cycle, d flood Indicates the water flow direction deviation value at the end of the acquisition cycle, d normal Indicates the deviation value of the water flow direction at the start time of the acquisition cycle;

[0064] The power supply damage fluctuation coefficient is an important indicator used to measure the degree of damage and fluctuation of the local data center power supply system caused by disasters such as floods. It is used to evaluate the stability and reliability of the power supply system and to quantify the degree of damage and fluctuation of the local data center power supply system during floods. It reflects the stability and recovery ability of the power supply system during disasters. The larger the power supply damage fluctuation coefficient, the greater the degree of damage and abnormal fluctuation of the local data center power supply system during floods, and the greater the probability of deploying a mobile data recovery center.

[0065] Specifically, the higher power supply damage fluctuation coefficient reflects the following problems:

[0066] Long power outage duration: A higher total power outage duration means that the local data center loses power for a longer period of time during a disaster, affecting the normal operation of the local data center;

[0067] Operational interruption: Prolonged power outages can cause business interruptions in local data centers, affecting data processing and service continuity;

[0068] Large voltage fluctuations and unstable voltage: A larger voltage fluctuation range indicates that the power supply system is unstable during a disaster, which may cause damage to local data center equipment;

[0069] Equipment damage risk: Voltage fluctuations may cause damage or performance degradation to servers, storage devices, and other critical equipment;

[0070] Long power restoration time and long recovery time: Long power restoration time indicates that the power supply system takes a long time to return to normal after a disaster, prolonging the operation interruption time of the local data center.

[0071] Low restoration efficiency: The power supply system has low restoration efficiency, which may require more resources and time to repair;

[0072] Long backup power operation time and backup power dependence: Long backup power operation time means that during the main power outage, the local data center needs to rely on the backup power supply for a longer time.

[0073] High pressure on backup power supply: Long-term reliance on backup power supply may increase the pressure on the backup power supply system, leading to wear and tear of backup power supply equipment;

[0074] Therefore, by obtaining the damage degree information of the local data center, the damage degree and fluctuation of the local data center power supply system during the flood are analyzed, and the power supply damage fluctuation coefficient is obtained to measure the damage degree and fluctuation degree of the local data center power supply system caused by the flood;

[0075] The logic for obtaining the power supply damage fluctuation coefficient is as follows:

[0076] Set the period for obtaining damage information of the local data center;

[0077] The total power outage time, standard deviation of power supply voltage, power supply restoration time, and backup power supply operation time of the local data center are obtained during the acquisition cycle;

[0078] It should be noted that the total power outage time, power restoration time, and backup power supply operation time can be obtained through the power supply monitoring system. The standard deviation of the power supply voltage can be obtained by recording the voltage values ​​at different times during the cycle through the voltage monitoring device, and the average power supply voltage can be calculated based on the power supply voltage values ​​at different times. The expression is as follows: Where dy t represents the voltage value at time t, T represents the acquisition period, pjz represents the average supply voltage, and the standard deviation of the supply voltage is calculated based on the average supply voltage. The expression is as follows

[0079]

[0080] The power supply damage fluctuation coefficient is calculated based on the total power outage time, the standard deviation of the power supply voltage, the power supply recovery time, and the backup power supply operation time. The expression is as follows In the formula, gdz represents the total power interruption time, T represents the acquisition period, gdb represents the standard deviation of the power supply voltage, and gdb z Indicates the standard deviation of the preset power supply voltage, ghs indicates the power supply recovery time, and byd indicates the backup power supply operation time;

[0081] The internal humidity rise rate is an important indicator used to measure the speed at which the internal humidity of the local data center rises during a flood disaster. It is used to evaluate the impact of the flood on the internal humidity of the local data center. The greater the internal humidity rise rate, the faster the internal humidity of the local data center rises during the flood disaster, and the greater the probability of deploying a mobile data recovery center.

[0082] The faster the humidity rises inside a local data center, the more likely it is that:

[0083] The environment deteriorates rapidly and humidity increases rapidly: This indicates that external moisture quickly penetrates into the local data center, causing the environmental humidity to increase rapidly;

[0084] Environmental control failure: This may indicate that the existing environmental control system (such as air conditioning and dehumidification equipment) cannot effectively cope with sudden humidity changes;

[0085] Increased risk of equipment getting wet and electronic equipment getting damaged: Rapidly rising humidity can cause condensation inside electronic equipment, which may cause problems such as short circuits, corrosion, and component failures;

[0086] Damage to data storage devices: Hard disks, memory devices and other devices are more susceptible to moisture damage in high humidity environments, affecting data security;

[0087] Business continuity is affected and downtime risk increases: Rapidly rising humidity may lead to more equipment failures, thereby increasing the risk of downtime and affecting business continuity;

[0088] Degraded service quality: Equipment performance is affected by humidity, which may lead to longer service response time and reduced stability, thus affecting user experience and service quality;

[0089] Therefore, by obtaining the damage degree information of the local data center, the increase rate of humidity inside the local data center during the flood disaster is analyzed, and the internal humidity increase rate is obtained to measure the increase degree of humidity inside the local data center during the flood;

[0090] The calculation expression of the internal humidity rise rate is as follows Where T represents the acquisition period, sd1 represents the humidity at the start of the acquisition period, and sd2 represents the humidity at the end of the acquisition period;

[0091] The water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are normalized, and a deployment timing prediction model is constructed based on the normalized water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate to generate a deployment timing index sjz. The model is based on the following formula: Where a1, a2, and a3 represent the preset proportional coefficients of the water flow anomaly coefficient, the power supply damage fluctuation coefficient, and the internal humidity rise rate, respectively, and a1, a2, and a3 are all greater than 0;

[0092] From the above calculation expression, it can be seen that the larger the water flow anomaly coefficient, the larger the power supply damage fluctuation coefficient, and the larger the internal humidity rise rate, the larger the deployment opportunity index, indicating that the probability of deploying a mobile data recovery center is greater. Conversely, the smaller the water flow anomaly coefficient, the smaller the power supply damage fluctuation coefficient, and the smaller the internal humidity rise rate, the smaller the deployment opportunity index, indicating that the probability of deploying a mobile data recovery center is smaller.

[0093] Step S2, comparing the deployment timing index with the deployment timing index threshold to determine whether a mobile data recovery center needs to be deployed, and when a mobile data recovery center needs to be deployed, performing a site selection operation on the mobile data recovery center and deploying the mobile data recovery center in the selected deployment area;

[0094] The deployment timing index is compared with the deployment timing index threshold to determine whether a mobile data recovery center needs to be deployed, as follows:

[0095] If the deployment timing index is greater than the deployment timing index threshold, it indicates that the flood has a significant impact on the local data center and a mobile data recovery center needs to be deployed;

[0096] If the deployment timing index is less than or equal to the deployment timing index threshold, it indicates that the impact of the flood on the local data center is small, and there is no need to deploy a mobile data recovery center immediately. You can continue to monitor the situation and prepare emergency plans;

[0097] When it is necessary to deploy a mobile data recovery center, a site selection operation is performed for the mobile data recovery center. First, based on the existing flood warning information and historical data, the impact range of the flood is predicted, the affected area is determined, and the unaffected area is marked as the area to be selected. The area to be selected is divided and labeled according to the size of the area required for deploying the mobile data recovery center. The area to be selected after the numbering is comprehensively evaluated according to the altitude of the area, power supply conditions, and traffic conditions. The area to be selected after the numbering is sorted from large to small, and the area to be selected with the highest ranking is selected as the deployment area of ​​the mobile data recovery center. The necessary personnel, including technical personnel, transportation personnel, security personnel, etc., are deployed to transport the equipment required by the mobile data recovery center, including servers, network equipment, backup power supplies, air conditioning equipment, etc., to the selected deployment area, and the mobile data recovery center is deployed for testing.

[0098] Step S3, obtaining regional network communication information and data recovery importance information of the deployment area, comprehensively analyzing the immediate activation timing of the mobile data recovery center for data recovery, and obtaining an activation timing index;

[0099] The regional network communication information includes the regional network communication fluctuation coefficient, and the importance of data recovery information includes the importance ratio of data recovery;

[0100] The regional network communication fluctuation coefficient and data recovery importance ratio are marked as qyw and szy respectively;

[0101] The regional network communication fluctuation coefficient is an important indicator used to measure the stability and fluctuation of network communication in the deployment area. It can help determine the reliability of regional network communication in the event of a flood disaster, thereby guiding the timing of activating the mobile data recovery center. The larger the regional network communication fluctuation coefficient, the greater the fluctuation of network communication in the deployment area, the worse the stability, and the smaller the probability of immediately activating the mobile data recovery center. Conversely, the smaller the regional network communication fluctuation coefficient, the smaller the fluctuation of network communication in the deployment area, the higher the stability, and the greater the probability of immediately activating the mobile data recovery center.

[0102] The logic for obtaining the regional network communication fluctuation coefficient is as follows:

[0103] Obtain network communication data in the deployment area within the K time period, including but not limited to packet delay and packet loss rate;

[0104] It should be noted that packet delay and packet loss rate can be obtained in real time by using professional network monitoring tools such as Nagios and Zabbix. These tools can provide detailed information about packet delay and packet loss rate.

[0105] Calculate the average packet delay at different times The expression is as follows Where L i represents the packet delay at the i-th moment;

[0106] Calculate the average packet loss rate at different times The expression is as follows In the formula represents the packet loss rate at the i-th moment;

[0107] Calculate the regional network communication fluctuation coefficient, the expression is as follows

[0108] The data recovery importance ratio is an indicator used to measure the urgency of data recovery in the damaged local data center. It is used to determine whether it is necessary to immediately activate the mobile data recovery center to recover the data in the event of a flood disaster, so as to minimize business interruption and data loss. The greater the data recovery importance ratio, the more urgent the data recovery in the damaged local data center is, and the greater the probability of immediately activating the mobile data recovery center to recover the data.

[0109] The calculation expression for the importance ratio of data recovery is as follows Where F1 represents the amount of online data access to the damaged local data center within the K time period, and F2 represents the amount of data storage in the damaged local data center within the K time period;

[0110] The regional network communication fluctuation coefficient and the proportion of data recovery importance are normalized, and an activation timing prediction model is constructed based on the normalized regional network communication fluctuation coefficient and the proportion of data recovery importance to generate an activation timing index qsz. The model is based on the following formula: Where b1 and b2 represent the regional network communication fluctuation coefficient and the preset proportional coefficient of the importance of data recovery, respectively, and b1 and b2 are both greater than 0;

[0111] From the above calculation expression, it can be seen that the smaller the regional network communication fluctuation coefficient and the greater the proportion of data recovery importance, the greater the activation opportunity index, indicating that the probability of immediately activating the mobile data recovery center is greater; conversely, the larger the regional network communication fluctuation coefficient and the smaller the proportion of data recovery importance, the smaller the activation opportunity index, indicating that the probability of immediately activating the mobile data recovery center is smaller;

[0112] Step S4, comparing the activation timing index with the activation timing index threshold, and determining the immediate activation timing of the mobile data recovery center for data recovery, the specific situation is as follows:

[0113] If the activation timing index is greater than the activation timing index threshold, it indicates that the network communication fluctuation in the current deployment area is small, the stability is high, and the urgency of data recovery in the damaged local data center is high. The mobile data recovery center needs to be activated immediately to recover the data in the damaged local data center.

[0114] If the activation timing index is less than or equal to the activation timing index threshold, it indicates that the network communication fluctuation in the current deployment area is large, the stability is poor, and the urgency of data recovery in the damaged local data center is low. Immediately activating the mobile data recovery center to recover the data in the damaged local data center may result in low data recovery efficiency and increase the risk of data loss or damage. Therefore, there is no need to immediately activate the mobile data recovery center to recover the data in the damaged local data center.

[0115] The present invention uses flood impact range information and damage degree information of a local data center after a flood disaster occurs, and comprehensively analyzes the deployment timing of a mobile data recovery center according to the flood impact range information and the damage degree information of a local data center, and compares the obtained deployment timing index with a deployment timing index threshold to determine the deployment timing of the mobile data recovery center, thereby avoiding waste of resources caused by blind deployment, making the deployment timing more accurate and efficient, and ensuring that the mobile data recovery center is put into use at the most appropriate time after a flood disaster occurs. The present invention also obtains regional network communication information and data recovery importance information of a deployment area, comprehensively analyzes the immediate activation timing of data recovery in the mobile data recovery center, and compares the obtained activation timing index with an activation timing index threshold to flexibly determine the startup time of the recovery work according to actual conditions, thereby avoiding adverse effects caused by too early or too late startup, ensuring the integrity and availability of geographic information surveying and mapping data, and increasing the fault tolerance of geographic information surveying and mapping data management under flood disasters.

[0116] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for intelligent management of geographic information surveying and mapping data, characterized in that: The steps include: Step S1, obtaining flood impact range information and damage degree information of the local data center after the flood disaster occurs, performing a comprehensive analysis on the deployment timing of the mobile data recovery center according to the flood impact range information and the damage degree information of the local data center, and obtaining a deployment timing index; Step S2, comparing the deployment timing index with the deployment timing index threshold to determine whether a mobile data recovery center needs to be deployed, and when a mobile data recovery center needs to be deployed, performing a site selection operation on the mobile data recovery center and deploying the mobile data recovery center in the selected deployment area; Step S3, obtaining regional network communication information and data recovery importance information of the deployment area, comprehensively analyzing the immediate activation timing of the mobile data recovery center for data recovery, and obtaining an activation timing index; Step S4, comparing the activation timing index with the activation timing index threshold, and determining the immediate activation timing of the mobile data recovery center for data recovery.

2. The method for intelligent management of geographic information surveying and mapping data according to claim 1, characterized in that: The flood impact range information includes the water flow anomaly coefficient, and the damage degree information of the local data center includes the power supply damage fluctuation coefficient and the internal humidity rise rate; The water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are marked as sly, gds, and nbs, respectively.

3. The method for intelligent management of geographic information surveying and mapping data according to claim 2, characterized in that: The logic for obtaining the water flow anomaly coefficient is as follows: Set the period for obtaining flood impact range information; At the start and end of the acquisition cycle, the water flow, water flow velocity, and water flow direction deviation values ​​within a preset area around the local data center after the flood occurs are respectively obtained; The water flow anomaly coefficient is calculated based on the water flow rate, water flow velocity and water flow direction deviation values ​​at the start and end of the acquisition cycle. The expression is as follows Where q flood Indicates the water flow at the end of the acquisition cycle, q normal Indicates the water flow at the start of the acquisition cycle, v flood Indicates the water velocity at the end of the acquisition cycle, v normal Indicates the water flow velocity at the start of the acquisition cycle, d flood Indicates the water flow direction deviation value at the end of the acquisition cycle, d normal Indicates the deviation value of the water flow direction at the start time of the acquisition cycle; The logic for obtaining the power supply damage fluctuation coefficient is as follows: Set the period for obtaining damage information of the local data center; The total power outage time, standard deviation of power supply voltage, power supply restoration time, and backup power supply operation time of the local data center are obtained during the acquisition cycle; The power supply damage fluctuation coefficient is calculated based on the total power outage time, the standard deviation of the power supply voltage, the power supply recovery time, and the backup power supply operation time. The expression is as follows In the formula, gdz represents the total power interruption time, T represents the acquisition period, gdb represents the standard deviation of the power supply voltage, and gdb z Indicates the standard deviation of the preset power supply voltage, ghs indicates the power supply recovery time, and byd indicates the backup power supply operation time; The calculation expression of the internal humidity rise rate is as follows Where T represents the acquisition period, sd1 represents the humidity at the start of the acquisition period, and sd2 represents the humidity at the end of the acquisition period.

4. The method for intelligent management of geographic information surveying and mapping data according to claim 3, characterized in that: The water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate are normalized, and a deployment timing prediction model is constructed based on the normalized water flow anomaly coefficient, power supply damage fluctuation coefficient, and internal humidity rise rate to generate a deployment timing index sjz. The model is based on the following formula: Wherein a1, a2, and a3 represent the preset proportional coefficients of the water flow anomaly coefficient, the power supply damage fluctuation coefficient, and the internal humidity rise rate, respectively, and a1, a2, and a3 are all greater than 0.

5. The method for intelligent management of geographic information surveying and mapping data according to claim 4, characterized in that: The deployment timing index is compared with the deployment timing index threshold to determine whether a mobile data recovery center needs to be deployed, as follows: If the deployment timing index is greater than the deployment timing index threshold, a mobile data recovery center needs to be deployed; If the deployment timing index is less than or equal to the deployment timing index threshold, there is no need to deploy the mobile data recovery center immediately.

6. The method for intelligent management of geographic information surveying and mapping data according to claim 5, characterized in that: The regional network communication information includes the regional network communication fluctuation coefficient, and the importance of data recovery information includes the importance ratio of data recovery; The regional network communication fluctuation coefficient and data recovery importance ratio are marked as qyw and szy respectively.

7. The method for intelligent management of geographic information surveying and mapping data according to claim 6, characterized in that: The logic for obtaining the regional network communication fluctuation coefficient is as follows: Obtain network communication data in the deployment area within the K time period, including but not limited to packet delay and packet loss rate; Calculate the average packet delay at different times The expression is as follows Where L i represents the packet delay at the i-th moment; Calculate the average packet loss rate at different times The expression is as follows In the formula represents the packet loss rate at the i-th moment; Calculate the regional network communication fluctuation coefficient, the expression is as follows 8. The method for intelligent management of geographic information surveying and mapping data according to claim 7, characterized in that: The calculation expression for the importance ratio of data recovery is as follows Where F1 represents the amount of online data access to the damaged local data center within the K time period, and F2 represents the amount of data storage in the damaged local data center within the K time period.

9. The method for intelligent management of geographic information surveying and mapping data according to claim 8, characterized in that: The regional network communication fluctuation coefficient and the proportion of data recovery importance are normalized, and an activation timing prediction model is constructed based on the normalized regional network communication fluctuation coefficient and the proportion of data recovery importance to generate an activation timing index qsz. The model is based on the following formula: Where b1 and b2 represent the regional network communication fluctuation coefficient and the preset proportional coefficient of the importance of data recovery, respectively, and both b1 and b2 are greater than 0.

10. The method for intelligent management of geographic information surveying and mapping data according to claim 9, characterized in that: The activation timing index is compared with the activation timing index threshold to determine the immediate activation timing of the mobile data recovery center for data recovery, as follows: If the activation timing index is greater than the activation timing index threshold, the mobile data recovery center needs to be immediately activated to recover the data in the damaged local data center; If the activation timing index is less than or equal to the activation timing index threshold, there is no need to immediately activate the mobile data recovery center to recover the data in the damaged local data center.

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