A natural resource data service and model dynamic update method and system

Abstract

The present invention relates to the field of data services, and specifically to a method and system for dynamic updating of natural resource data services and models, including: step 1: calculating the weight of a business system, and calculating the weight of the corresponding business system according to the activity and importance of each business system; step 2: calculating the data access heat, and obtaining the data access heat by following up the spatial range and frequency of the layers within the business system range through grid processing; step 3: calculating the dynamic priority, and integrating the priorities of all business systems and the heat of the region, and multiplying the business system priority and the heat grid correspondingly to obtain a total priority grid, and normalizing to obtain the final regional heat; step 4: data update, superimposing the received new data and the priority grid, adding them to the update queue, and allocating computing resources to the tasks in order of priority. This technical solution can realize real-time updating of calculation results, and provide an important reference for later data procurement and improving user experience.

Description

Technical Field

[0001] The present invention relates to the field of data services, and in particular to a natural resource data service and model dynamic updating method and system. Background Art

[0002] The "One Map of Natural Resources" system integrates new technologies such as the Internet of Things and cloud computing to build a unified, authoritative, and dynamically updated three-dimensional big data system. The system brings together global resources, realizes data classification, coding, and sharing, and provides rich data resources for various departments. In land space planning, the system supports planning preparation, approval, implementation, and supervision to ensure that the planning is scientific and authoritative. At the same time, it monitors resource conditions in real time, provides disaster warning, indicator supervision and other functions, and realizes refined and intelligent management. Three-dimensional visualization analysis assists decision-making, such as optimizing project site selection and improving approval efficiency. With powerful data services and model support, the "One Map of Natural Resources" system empowers many business systems and promotes natural resource management to a new level of digitalization and intelligence.

[0003] The data of a graph serves many business systems. Each business system is independent and sensitive to business information. A graph cannot obtain sensitive information of a business system. In addition, the model data generally requires a large amount of calculation and a large amount of time to update in batches, so it is generally updated regularly. This causes a certain lag in the model data. The update process involves a lot of data, repeated steps, and the data cannot be updated in real time, which affects business processing. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide an update method and system which obtains less business system information, estimates the hot spots of the business, and uses limited computing resources to give priority to updating the data in the hot spots, thereby improving the data update efficiency of a map system to a near real-time update frequency.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is: a natural resource data service and model dynamic update method, comprising the following steps:

[0006] Step 1: Calculate the weight of the business system. Send the non-sensitive statistical data of the business system within the T1 period to each business system at the sampling period T1, and calculate the weight of the corresponding business system according to the activity and importance of each business system.

[0007] Step 2: Calculate the data access heat. Through grid processing, obtain the re-grid and grid quantity, accumulate the grid heat values, follow up the spatial range and frequency of the layer within the business system scope, calculate the heat of the hot spot area or area corresponding to the business system, and obtain the data access heat;

[0008] Step 3: Calculate the dynamic priority, integrate all business system priorities and the heat of the region, multiply the business system priority and heat grids to get a total priority grid, normalize the priority grid, and get the final regional heat;

[0009] Step 4: Data update: superimpose the received new data with the priority grid, add it to the update queue, and allocate computing resources to the tasks in order of priority.

[0010] In the above-mentioned natural resource data service and model dynamic update method, the non-sensitive statistical data include the number of active users, the number of business transactions, and the total of business importance scores.

[0011] In the above-mentioned natural resource data service and model dynamic update method, the active user number is the number of users who have data requests within the time period, the business processing number refers to the sum of the business links when the data request is made, and the sum of the business importance scores is the sum of the importance scores of the businesses that issue data requests.

[0012] In the above-mentioned natural resource data service and model dynamic update method, the business importance categories in the total business importance score include urgent and important, important but not urgent, urgent but not important, and neither urgent nor important, and a score is set for each category.

[0013] In the above-mentioned natural resource data service and model dynamic updating method, step 1 comprises:

[0014] Step 1-1: Storing the non-sensitive statistical data into a data set, wherein:

[0015] U={u1,u2,..u i ,.,u N} i is the number of active users of the i-th business system,

[0016] P={p1,p2,..p i ,.,p N} i is the number of business transactions of the i-th business system,

[0017] E={e1,e2,..e i ,.,e N} i is the sum of the business importance of the i-th business system;

[0018] Step 1-2: Normalize U, P, and E.

[0019] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. The maximum number of active users in all business systems;

[0020] , ∈[0,1], represents the number of transactions handled by the i-th business system, The minimum number of transactions handled by all business systems. The maximum number of businesses handled by all business systems;

[0021] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. The maximum number of active users in all business systems;

[0022] Get the normalized value U of the non-sensitive statistical data norm , P norm 、E norm ;

[0023] Step 1-3: Use the normalized value of the non-sensitive statistical data to calculate the entropy of the corresponding non-sensitive statistical data.

[0024] ,in ;

[0025] ,in ;

[0026] ,in , N is the total number of business systems;

[0027] Step 1-4: Calculate the weight of the corresponding non-sensitive statistical data using the entropy of the corresponding non-sensitive statistical data,

[0028] ,

[0029] ,

[0030] ;

[0031] Step 1-5: Calculate the priority of the business system using the weights corresponding to the non-sensitive statistical data.

[0032] L i = * + * + * , L i Indicates the priority of the i-th business system.

[0033] In the above-mentioned natural resource data service and model dynamic updating method, step 2 comprises:

[0034] Step 2-1: Divide all data coverage areas into square grids of uniform size, and create a grid for calculating heat values ​​for each business system;

[0035] Step 2-2: When receiving a service request from the business system, extract the data range frame of the service request;

[0036] Step 2-3: Overlay the rectangle corresponding to the data range frame and the corresponding business system grid;

[0037] Step 2-4: Get all grids and their numbers covered by the requested range rectangle;

[0038] Step 2-5: Add 1 / Z to the heat value of each grid that is covered, that is, G i,j,k +=1, indicating that the heat value of the grid in the jth row and kth column of the i-th business system is accumulated as 1 / Z.

[0039] The above-mentioned natural resource data service and model dynamic update method, step 3 includes: priority grid , normalize the priority grid P to (0,100], , Corresponding to the maximum and minimum values ​​of all grid priorities.

[0040] In the above-mentioned natural resource data service and model dynamic update method, in step 4, when data are superimposed, the value with the largest priority is used as the priority of the current batch of data.

[0041] The above-mentioned natural resource data service and model dynamic update method, in step 4, also includes giving priority to the purchase or collection of data in hot spot areas according to the distribution of business hot spots.

[0042] A natural resource data service and model dynamic update system, the system is operated using any of the natural resource data service and model dynamic update methods as described above, comprising:

[0043] The business system weight calculation module sends non-sensitive statistical data of the business system within the T1 period to each business system at the sampling period T1, and calculates the weight of the corresponding business system according to the activity and importance of each business system;

[0044] The data access heat calculation module obtains the re-grid and grid quantity through grid processing, accumulates the grid heat values, follows up the spatial range and frequency of the layers within the business system scope, calculates the heat of the hot spot area or area corresponding to the business system, and obtains the data access heat;

[0045] The dynamic priority calculation module integrates all business system priorities and regional heat, multiplies the business system priorities and heat grids to obtain a total priority grid, normalizes the priority grid, and obtains the final regional heat;

[0046] The data update module superimposes the received new data with the priority grid, adds it to the update queue, and allocates computing resources to tasks in order of priority.

[0047] The beneficial effects of the method and system for dynamically updating natural resource data services and models of the present invention are as follows: the technical solution obtains less business system information, superimposes the rectangle corresponding to the calculation data range box and the corresponding business system grid, estimates the hotspot area of ​​the business, and uses limited computing resources to implement priority update of the hotspot area data, thereby improving the data update efficiency of the natural resource data service system and achieving a near real-time update frequency. Moreover, the technical solution is not affected by sensitive information, estimates the priorities and focus points of different business systems, and can not only realize real-time update of calculation results, but also provide important references for later data procurement and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a business system weight calculation module in an embodiment of the present invention;

[0049] Figure 2 This is a flow chart of a data access heat calculation module in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the dynamic priority calculation module flow in an embodiment of the present invention;

[0051] Figure 4 Schematic diagram of the data update and computing resource management module flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is described below in conjunction with specific implementation methods and drawings.

[0053] Example 1

[0054] like Figure 1 As shown, a natural resource data service and model dynamic update method includes the following steps.

[0055] Step 1: Calculate the weight of the business system, and send non-sensitive statistical data of the business system within the T1 period to each business system with a sampling period of T1. The non-sensitive statistical data includes the number of active users, the number of business transactions, and the sum of the business importance scores. The number of active users refers to the number of users who have data requests during the period, the number of business transactions refers to the sum of the business links at the time of the data request, and the sum of the business importance scores is the sum of the importance scores of the businesses that issued the data request.

[0056] The business importance categories in the total business importance score include urgent and important, important but not urgent, urgent but not important, and neither urgent nor important, and a score is set for each category.

[0057] Calculate the weight of each business system based on its activity level and business importance.

[0058] include:

[0059] Step 1-1: Storing the non-sensitive statistical data into a data set.

[0060] Among them, U={u1,u2,..u i ,.,u N} i is the number of active users of the i-th business system,

[0061] P={p1,p2,..p i ,.,p N} i is the number of business transactions of the i-th business system,

[0062] E={e1,e2,..e i ,.,e N} i is the sum of the business importance of the i-th business system.

[0063] Step 1-2: Normalize U, P, and E.

[0064] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. It is the maximum number of active users in all business systems.

[0065] , ∈[0,1], represents the number of transactions handled by the i-th business system, The minimum number of transactions handled by all business systems. The maximum number of transactions handled by all business systems.

[0066] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. It is the maximum number of active users in all business systems.

[0067] Get the normalized value U of the non-sensitive statistical data norm , P norm 、E norm。

[0068] Step 1-3: Calculate the entropy of the corresponding non-sensitive statistical data using the normalized value of the non-sensitive statistical data.

[0069] ,in ;

[0070] ,in ;

[0071] ,in , N is the total number of business systems.

[0072] Step 1-4: Calculate the weight of the corresponding non-sensitive statistical data using the entropy of the corresponding non-sensitive statistical data.

[0073] ,

[0074] ,

[0075] .

[0076] Step 1-5: Calculate the priority of the business system using the weights corresponding to the non-sensitive statistical data.

[0077] L i = * + * + * , L i Indicates the priority of the i-th business system.

[0078] Step 2: Calculate the data access heat. Through grid processing, obtain the number of re-grids and grids G, accumulate the grid heat values, follow up the spatial range and frequency of the layers within the business system scope, calculate the heat of the hot spot area or area corresponding to the business system, and obtain the data access heat.

[0079] include:

[0080] Step 2-1: Divide all data coverage areas into square grids of uniform size and create a grid for calculating heat values ​​for each business system.

[0081] Step 2-2: When a service request is received from the business system, extract the data range frame of the service request.

[0082] Step 2-3: Overlay the rectangle corresponding to the data range box and the corresponding business system grid.

[0083] Step 2-4: Get all grids and their numbers covered by the requested range rectangle.

[0084] Step 2-5: Add 1 / N to the G heat value of each grid that is covered, that is, G i,j,k +=1, indicating that the heat value of the grid in the jth row and kth column of the i-th business system is accumulated to 1 / N.

[0085] Step 3: Calculate the dynamic priority, integrate all business system priorities and the heat of the region, multiply the business system priority and heat grids accordingly to get a total priority grid, normalize the priority grid, and get the final regional heat.

[0086] Includes: Priority grid is , normalize the priority grid P to (0,100], , Corresponding to the maximum and minimum values ​​of all grid priorities, Represents the heat grid of the i-th business system.

[0087] Step 4: Data update: superimpose the received new data with the priority grid, add it to the update queue, and allocate computing resources to the tasks in order of priority.

[0088] When data is superimposed, the value with the highest priority is used as the priority of this batch of data.

[0089] In order to save costs and improve user experience, you can also prioritize data procurement or collection in hot spots based on the distribution of business hot spots.

[0090] Example 2

[0091] A natural resource data service and model dynamic update system can be applied to the "one map of natural resources" system, including a business system weight calculation module, a data access heat calculation module, a dynamic priority calculation module, and a data update management module. The system uses the natural resource data service and model dynamic update method recorded in Example 1 to operate.

[0092] The business system weight calculation module sends non-sensitive statistical data of the business system within the T1 period to each business system with a sampling period of T1, and calculates the weight of the corresponding business system according to the activity and importance of each business system.

[0093] The data access heat calculation module obtains the re-grids and grid numbers through grid processing, accumulates the grid heat values, follows up the spatial range and frequency of the layers within the business system scope, calculates the heat of the hot spots or areas corresponding to the business system, and obtains the data access heat.

[0094] The dynamic priority calculation module integrates all business system priorities and regional heat, multiplies the business system priorities and heat grids accordingly to obtain a total priority grid, normalizes the priority grid, and obtains the final regional heat.

[0095] The data update module superimposes the received new data with the priority grid, adds it to the update queue, and allocates computing resources to tasks in order of priority.

[0096] Specifically, the functions and calculation methods of each module are described as follows.

[0097] 1. Business system weight calculation module, such as Figure 1 shown.

[0098] The system has two scheduled tasks. The business system scheduled task regularly counts the number of active users, the number of business transactions, and other information, and sends it to the one-picture system. The one-picture scheduled task stores the statistical information sent by all business scheduled tasks into the queue and calculates the latest weight of the business system.

[0099] This module sends non-sensitive statistical data of the business system within the T1 period to each business system at a sampling period of T1, such as the number of active users, the number of business transactions, and the total business importance score.

[0100] ① The number of active businesses is the number of users who requested data during the period.

[0101] ② Number of business transactions: refers to the total number of business links involved when the data is requested.

[0102] ③ Total business importance scores: sum of the importance scores of the businesses that issue data requests.

[0103] Business classification is determined by each business system user department based on business circumstances.

[0104] Business importance is categorized as follows: urgent and important, important but not urgent, urgent but not important, and neither urgent nor important, with appropriate scores set for each category.

[0105] ④ Store the above data, including the number of active users of each business system, the number of business transactions, and the business importance score, into the data set.

[0106] U={u1,u2,..u i ,.,u N} i is the number of active users of the i-th business system.

[0107] P={p1,p2,..p i ,.,p N} i is the number of business transactions of the i-th business system.

[0108] E={e1,e2,..e i ,.,e N} i is the sum of the business importance of the i-th business system.

[0109] U represents the number of active users corresponding to N business systems.

[0110] P represents the number of business transactions corresponding to N business systems.

[0111] E represents the business importance scores corresponding to N business systems.

[0112] ⑤ Normalize U, P, and E.

[0113] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. It is the maximum number of active users in all business systems.

[0114] , ∈[0,1], represents the number of transactions handled by the i-th business system, The minimum number of transactions handled by all business systems. The maximum number of transactions handled by all business systems.

[0115] , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. It is the maximum number of active users in all business systems.

[0116] Through the above calculation, we can get the normalized value U of non-sensitive statistical data. norm , P norm 、E norm .

[0117] ⑥Calculate entropy.

[0118] ,in , i.e. specific gravity;

[0119] ,in , i.e. specific gravity;

[0120] ,in , that is, the proportion, and N is the total number of business systems. Through the above calculation, the entropy of the corresponding data is obtained.

[0121] ⑦Calculate the weight of the corresponding data.

[0122] ,

[0123] ,

[0124] , normalized here so that the sum of all weights equals 1.

[0125] ⑧Calculate the priority of the business system. The higher the priority of the business system, the more frequently accessed areas it is, which will have higher popularity and become the priority areas for data or model updates.

[0126] L i = * + * + * , L i Indicates the priority of the i-th business system.

[0127] 2. Data access heat calculation module, such as Figure 2 shown.

[0128] The system records the range of each map access request and stores it in a queue. The scheduled task calculates the heat value distribution of each business system corresponding to the grid based on the range in the queue. Finally, one business system corresponds to one heat grid.

[0129] ① Create a grid for calculating heat values ​​for each business system. The grid is to divide all data coverage areas (administrative areas) into square grids of uniform size. All these grids are like a fishing net, and the grid size is the same. i .

[0130] ②When receiving a service request from the business system, extract the data range box bbox of the service request.

[0131] ③Superimpose the rectangle corresponding to the bbox and the corresponding business system grid Gi.

[0132] ④ Get all the grids and their number Z covered by the requested range rectangle.

[0133] ⑤ Add 1 / Z, or G, to the heat value of each grid that is covered i,j,k +=1, the heat value of the grid in the jth row and kth column of the ith business system is accumulated as 1 / Z.

[0134] 3. Dynamic priority calculation module, such as Figure 3 shown.

[0135] After the business system heat grid is generated, the system performs spatial interpolation on all heat grids (commonly bilinear interpolation). In order to integrate the heat grids of each business system and the importance (weight) of the business system, the business system weight is multiplied by the business system heat grid, and then the corresponding grids are summed to obtain the priority grid.

[0136] ①Multiply the business system priority and the heat grid to get a total priority grid .

[0137] ②P grid is normalized to (0,100].

[0138] , Corresponding to the maximum and minimum values ​​of all grid priorities.

[0139] 4. Data update and computing resource management module, such as Figure 4 shown.

[0140] When there is a data update request, the data range and priority grid are superimposed to obtain the highest grid priority value within the data range, and inserted into the update queue according to the priority. The system executes data update tasks in order of priority.

[0141] ① When new data is received, the data and priority grid are superimposed, and the value with the highest priority is used as the priority of this batch of data.

[0142] ②Then join the update queue and allocate computing resources to tasks in order of priority.

[0143] ③ Similarly, according to the distribution of business hot spots, data procurement or collection in hot spots can be prioritized. This can save costs and improve user experience.

[0144] The above embodiments are only for illustrating the inventive concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A natural resource data service and model dynamic update method, characterized in that: The following steps are involved: Step 1: Calculate the weight of the business system, and send the non-sensitive statistical data of the business system within the T1 period to each business system with a sampling period of T1. The non-sensitive statistical data includes the number of active users, the number of business transactions, and the sum of the business importance scores. The entropy of the corresponding non-sensitive statistical data is used to calculate the weight of the corresponding non-sensitive statistical data, and the weight of the corresponding non-sensitive statistical data is used to calculate the priority of the business system; Step 2: Calculate the data access heat. Through grid processing, obtain the re-grid and grid quantity, accumulate the grid heat values, follow up the spatial range and frequency of the layers within the business system scope, calculate the heat of the hot spot area or area corresponding to the business system, and obtain the data access heat, including: Step 2-1: Divide all data coverage areas into square grids of uniform size, and create a grid for calculating heat values ​​for each business system; Step 2-2: When receiving a service request from the business system, extract the data range frame of the service request; Step 2-3: Overlay the rectangle corresponding to the data range frame and the corresponding business system grid; Step 2-4: Get all grids and their numbers covered by the requested range rectangle; Step 2-5: Add 1 / Z to the heat value of each grid that is covered, that is, G i,j,k +=1 / Z, which means the heat value of the grid in the jth row and kth column of the i-th business system is accumulated by 1 / Z; Step 3: Calculate the dynamic priority, integrate all business system priorities and the heat of the region, multiply the business system priority and heat grids to get a total priority grid, normalize the priority grid, and get the final regional heat; Step 4: Data update: superimpose the received new data and the priority grid. When superimposing data, the value with the largest priority is used as the priority of this batch of data, added to the update queue, and computing resources are allocated to tasks in order of priority.

2. The natural resource data service and model dynamic update method according to claim 1, characterized in that: The number of active users is the number of users who have data requests during the period, the number of business processing refers to the sum of the business links in which the data request is made, and the sum of the business importance scores is the sum of the importance scores of the businesses that issue the data requests.

3. The natural resource data service and model dynamic update method according to claim 2, characterized in that: The business importance categories in the total business importance score include urgent and important, important but not urgent, urgent but not important, and neither urgent nor important, and a score is set for each category.

4. The natural resource data service and model dynamic update method according to claim 3, characterized in that: The step 1 comprises: Step 1-1: Storing the non-sensitive statistical data into a data set, wherein: U={u1,u2,..u i ,.,u N } i is the number of active users of the i-th business system, P={p1,p2,..p i ,.,p N } i is the number of business transactions of the i-th business system, E={e1,e2,..e i ,.,e N } i is the sum of the business importance of the i-th business system; Step 1-2: Normalize U, P, and E. , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. The maximum number of active users in all business systems; , ∈[0,1], represents the number of transactions handled by the i-th business system, The minimum number of transactions handled by all business systems. The maximum number of businesses handled by all business systems; , ∈[0,1], represents the number of active users of the i-th business system, is the minimum number of active users of all business systems. The maximum number of active users in all business systems; Get the normalized value U of the non-sensitive statistical data norm , P norm 、E norm ; Step 1-3: Use the normalized value of the non-sensitive statistical data to calculate the entropy of the corresponding non-sensitive statistical data. ,in ; ,in ; ,in , N is the total number of business systems; Step 1-4: Calculate the weight of the corresponding non-sensitive statistical data using the entropy of the corresponding non-sensitive statistical data, ， ， ； Step 1-5: Calculate the priority of the business system using the weights corresponding to the non-sensitive statistical data. L i = * + * + * , L i Indicates the priority of the i-th business system.

5. The natural resource data service and model dynamic update method according to claim 4, characterized in that: The step 3 includes: priority grid , normalize the priority grid B to (0,100], , Corresponding to the maximum and minimum values ​​of all grid priorities, represents the i-th priority grid, Represents the heat grid of the i-th business system.

6. The natural resource data service and model dynamic update method according to claim 5, characterized in that: In the step 4, it also includes giving priority to the purchase or collection of data in hotspot areas according to the distribution of business hotspots.

7. A natural resource data service and model dynamic update system, characterized in that: The system is operated using the natural resource data service and model dynamic update method according to any one of claims 1 to 6, including: The business system weight calculation module sends non-sensitive statistical data of the business system within the T1 period to each business system at the sampling period T1, and calculates the weight of the corresponding business system according to the activity and importance of each business system; The data access heat calculation module obtains the re-grid and grid quantity through grid processing, accumulates the grid heat values, follows up the spatial range and frequency of the layers within the business system scope, calculates the heat of the hot spot area or area corresponding to the business system, and obtains the data access heat; The dynamic priority calculation module integrates all business system priorities and regional heat, multiplies the business system priorities and heat grids to obtain a total priority grid, normalizes the priority grid, and obtains the final regional heat; The data update module superimposes the received new data with the priority grid, adds it to the update queue, and allocates computing resources to tasks in order of priority.

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