Energy storage container hydrogen sensor layout optimization method based on simulation iteration

Abstract

The application discloses a kind of based on simulation iteration's energy storage container hydrogen sensor point distribution optimization method, comprising the following steps: step one, carry out global scanning and potential detection point identification, obtain the potential optimal point distribution of " most sensitive " to leakage source;Step two, implement local refinement analysis and determine candidate point, to identify " sensitive area " and select final candidate point distribution;Step three, after the final candidate point distribution selected in step two, carry out global optimization, and generate final point distribution scheme after global optimization is completed;Step four, using the final point distribution scheme generated in step three carries out simulation verification, while constructing point distribution database, and the optimal point distribution scheme that is verified passes is stored in point distribution database.The method of the application is improved by multi-stage iterative detection and optimization Sensitivity and reliability of hydrogen sensor point distribution, can quickly identify leakage source, provide strong guarantee for energy storage cabinet safe operation.

Description

Technical Field

[0001] This invention relates to the field of energy storage container safety monitoring technology, and more specifically to a simulation-based iterative optimization method for the placement of hydrogen sensors in energy storage containers. Background Technology

[0002] Energy storage containers, with lithium-ion batteries at their core, are widely used in grid peak shaving and distributed energy storage due to their flexible deployment and rapid response. However, during charge-discharge cycles, overcharge and over-discharge, high-temperature aging, or the pre-thermal runaway stages, the electrolyte inside the battery undergoes decomposition reactions, and the electrode materials also produce side reactions, releasing characteristic gases such as hydrogen. Therefore, hydrogen detection is currently an effective means of safety protection for energy storage containers. However, the internal space of energy storage containers is usually large, and accurate and reliable leak detection requires precise placement of hydrogen sensors. Current placement methods rely mainly on manual experience, lacking a scientific and systematic basis, which can easily lead to unreasonable sensor distribution and difficulty in fully covering potential leak areas. Furthermore, existing hydrogen sensing technologies suffer from poor selectivity, slow response speed, or insufficient accuracy, further reducing the reliability of leak detection and failing to meet the high requirements of warehouse safety monitoring. Therefore, there is an urgent need to optimize the hydrogen sensor placement scheme to improve detection effectiveness. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method for optimizing the placement of hydrogen sensors in energy storage containers based on simulation and iterative detection. By combining fire dynamics simulation with iterative detection, the method optimizes the placement of hydrogen sensors, thereby improving the accuracy and reliability of leak detection.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a simulation-based iterative optimization method for hydrogen sensor placement in energy storage containers, comprising the following steps: Step 1: Perform a global scan and identify potential detection points to obtain the most sensitive potential optimal deployment points for the leak source; Step two involves conducting localized, refined analysis and identifying candidate points to pinpoint "sensitive areas" and select the final candidate locations. Step 3: After the final candidate placement points are selected in Step 2, global optimization is performed, and the final placement plan is generated after the global optimization is completed. Step four: Use the final placement scheme generated in step three to perform simulation verification, and at the same time build a placement database, storing the verified optimal placement scheme into the placement database.

[0005] As a further improvement to the present invention, the specific method for global scanning in step one is as follows: First, a refined three-dimensional model is established: a 1:1 three-dimensional model of the energy storage container is established using fire dynamics software, including internal structures such as battery racks, battery modules, ventilation openings, air conditioning systems, and cable channels; then, leakage scenarios are set: multiple representative leakage scenarios are defined, including: leakage locations: battery pack connectors, module valves, and pipe connections; leakage rates: based on battery chemical reaction and safety valve data, small, medium, and large leakage rates are set; environmental conditions: considering the opening / closing of the ventilation system and ambient temperature; then, a "virtual sensor grid" is deployed: a large number of virtual sensors are uniformly arranged at a high density inside the energy storage cabinet; finally, simulation and data acquisition are run: transient simulations are run for each leakage scenario, and hydrogen concentration-time series data at each virtual sensor location are recorded.

[0006] As a further improvement of the present invention, the specific method for identifying potential detection points in step one is as follows: set a reliable detection concentration threshold, and for each leakage scenario, find the locations of several sensors that first exceed the threshold. These points are the potential optimal placement points that are "most sensitive" to the leakage source.

[0007] As a further improvement of the present invention, the specific method of implementing local fine-grained analysis in step two is as follows: Constructing "sensitive areas": Performing spatial clustering analysis on all "potential detection points" identified in the first stage to form several key "sensitive areas"; Local fine-grained simulation: Deploying a second round of virtual sensor grids at smaller intervals within each "sensitive area".

[0008] As a further improvement of the present invention, the specific method for determining the final candidate points in step two is as follows: run the leakage simulation again in the formed "sensitive area", considering not only potential detection points, but also reliability, robustness and concentration gradient. Based on these indicators, select 1-2 final candidate points in each "sensitive area".

[0009] As a further improvement of the present invention, the specific method for generating the final deployment scheme after global optimization in step three is as follows: adopt multi-objective optimization: treat all final candidate deployment points as a whole scheme, and consider the following constraints: limit on the total number of sensors, cover all key leakage scenarios, and avoid redundancy. Input these constraints into the optimization algorithm, optimize the whole scheme through the optimization algorithm, and select an optimal subset from the candidate points.

[0010] As a further improvement of the present invention, the specific method for optimizing the overall scheme through an optimization algorithm in step three and selecting an optimal subset from the candidate points is as follows: First, the importance of each valid cluster is assessed. The importance score is determined by two factors: the cluster size, i.e., the number of sensor detection points contained in the cluster; and the spatial compactness of the cluster. Spatial compactness is assessed by calculating the standard deviation of the coordinate values ​​of all points within the cluster on the three coordinate axes of the X container's long side, Y container's wide side, and Z container's high side. The smaller the standard deviation, the more concentrated the points are, and the higher the compactness. The compactness index is calculated using an inverse formula: Compactness = 1 / (1 + Mean Standard Deviation). The final importance score equals the cluster size multiplied by the compactness index. The larger this product, the more important the cluster region. After calculation, the system sorts all clusters from highest to lowest importance score. Core region center recommendation: The system selects the cluster with the highest importance score as the main leakage area. The recommended point location is directly taken as the geometric center of the cluster. That is, the average coordinates of all points in the cluster in the X, Y and Z dimensions are calculated as the coordinates of the recommended point. This point is marked as the "core region center". The description information includes the size value of the cluster. Secondary region center recommendation: If the number of clusters is greater than or equal to 2 and the number of current recommended points has not reached the preset limit, the system continues to select the cluster with the second highest importance score as the secondary leakage region. The recommended point location is also taken as the geometric center coordinate of the cluster. This point is marked as "secondary region center" and the description information includes the size value of the cluster. Strategic Coverage Point Recommendation: If the current number of recommended points has not yet reached the upper limit and there are at least two important clusters, the system performs strategic coverage point calculation. This strategy aims to optimize the coverage of the monitoring network. The system takes the coordinates of the center points of the two most important clusters and calculates the average coordinates of these two points in the X, Y, and Z dimensions. The resulting midpoint position is used as the recommended point. The cluster size of this point is the sum of the sizes of the two clusters, and the importance score is the arithmetic mean of the importance scores of the two clusters. This point is marked as a "strategic coverage point", and the description information indicates that it is a strategic location covering two main areas. Boundary expansion point recommendation: If the number of recommended points is still insufficient and there is at least one important cluster, the system performs boundary expansion point calculation. This strategy aims to detect the direction of leakage diffusion in advance. The system first calculates the coordinate range of the main cluster in the X-axis direction, and then adds 40% of this range value to the X-coordinate of the center point of the main cluster as the X-coordinate of the recommended point. The Y and Z coordinates remain unchanged. The cluster size of this point is the same as the original cluster, and the importance score is 80% of the importance score of the original cluster. This point is marked as a "boundary expansion point", and the description information indicates that it is the boundary expansion location of the main region. Supplementary monitoring point recommendation: If the number of recommended points still does not reach the preset limit after the above strategy, the system will perform supplementary monitoring point calculation. This strategy generates supplementary points based on the already recommended points. If the list of existing points is not empty, the system takes the coordinates of the first recommended point, adds 1.0 meter to its Y coordinate as the new Y coordinate of the recommended point, and keeps the X and Z coordinates unchanged. The cluster size of this point is the same as the base point, and the importance score is 60% of the importance score of the base point. If there are no already recommended points, but a cluster exists, the coordinates of the center point of the most important cluster are taken, and 1.0 meter is added to its X coordinate as the coordinates of the recommended point. The importance score is 60% of the original cluster importance score. This point is marked as a "supplementary monitoring point", and the description information indicates that it is a supplementary monitoring location. Finally, ensure that the final number of recommended points does not exceed the preset maximum number of recommended points. If it does, truncate the list and keep only the first N points. Output the detailed information of all recommended points in order, including: serial number, type, X / Y / Z coordinates and description information.

[0011] The beneficial effects of this invention are: Process innovation: An iterative point placement process of "global scanning → local refinement → global optimization" is proposed, which is different from the traditional one-time experience-based point placement method.

[0012] Technological innovation: Deeply integrate computational fluid dynamics (CFD) simulation (fire dynamics) with sensor optimization theory, use data to drive decision-making, and make the site selection plan have a clear scientific basis.

[0013] The concept of "virtual sensor mesh" involves using a high-density virtual mesh for global detection in simulation, which is an efficient and low-cost "digital twin" testing method.

[0014] Multi-index evaluation system: It not only considers "detection time", but also integrates multiple performance indicators such as "reliability", "robustness" and "concentration gradient", making the scheme closer to engineering practice.

[0015] Automation and knowledge accumulation: The entire process can be scripted and automated, forming a reusable deployment solution knowledge base, which has value for industrial application and promotion. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the given embodiments.

[0017] The simulation-based iterative method for optimizing the placement of hydrogen sensors on an energy storage container, as described in this embodiment, includes the following steps: Step 1: Perform a global scan and identify potential detection points to obtain the most sensitive potential optimal deployment points for the leak source; Step two involves conducting localized, refined analysis and identifying candidate points to pinpoint "sensitive areas" and select the final candidate locations. Step 3: After the final candidate placement points are selected in Step 2, global optimization is performed, and the final placement plan is generated after the global optimization is completed. Step four involves using the final placement scheme generated in step three for simulation verification. Simultaneously, a placement database is constructed, storing the verified optimal placement scheme. This method identifies potential placement points through global scanning, then filters candidate points through local refined analysis, and finally generates and verifies a global optimization scheme. This solves the problem of unreasonable placement based on human experience, achieves scientific placement, and improves the accuracy of leak detection.

[0018] Furthermore, the specific method for global scanning in step one is as follows: First, establish a refined 3D model: Use fire dynamics software to create a 1:1 3D model of the energy storage container, including internal structures such as battery racks, battery modules, ventilation openings, air conditioning systems, and cable channels; then, define leakage scenarios: Define multiple representative leakage scenarios, including: leakage locations: battery pack connectors, module valves, and pipe connections; leakage rates: Based on battery chemical reaction and safety valve data, set small, medium, and large leakage rates; environmental conditions: Consider the ventilation system's on / off state and ambient temperature; next, deploy a "virtual sensor grid": Distribute a large number of virtual sensors evenly and at a high density inside the energy storage cabinet; finally, run simulations and acquire data: Run transient simulations for each leakage scenario, recording hydrogen concentration-time series data at each virtual sensor location. This step, through realistic models and multi-scenario simulations, helps improve the realism and comprehensiveness of the deployment plan.

[0019] Furthermore, virtual detection points are placed at suitable sensor locations on the energy storage container. On the side walls of the container, a two-dimensional array of virtual points is arranged at a spacing of 0.5m, with 16 columns long and 3 rows wide; on the container roof, a three-dimensional array of virtual points is arranged at a spacing of 0.25m, with 16 columns long, 6 rows wide, and 3 rows high.

[0020] Run CFD simulations for each preset thermal runaway scenario (e.g., thermal runaway at different locations), and output hydrogen gas concentration and time-series data for each virtual point. Set a detection concentration threshold: adopt a low and reliable threshold, such as 10 ppm. This threshold must be higher than the environmental background concentration and the sensor's detection limit to achieve early warning.

[0021] Sensitive point identification algorithm: For each leakage scenario, scan the concentration data of all virtual points.

[0022] Identify the N (e.g., the first 10) virtual points that first exceed the 10 ppm threshold. Record the ID of these points, the time of the first exceedance (t_detect), and the concentration value at that time.

[0023] Furthermore, the specific method for implementing local refined analysis in step two is as follows: Constructing "sensitive areas": All "potential detection points" identified in the first stage are placed in a data point set and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is used to form several key "sensitive areas"; Key parameter settings: eps (neighborhood radius): Set according to container size and point spacing, for example, 0.8 meters. This indicates that points within this radius are considered neighbors. min_samples (minimum number of samples): For example, 3. This indicates the minimum number of points required to form a "dense region".

[0024] Localized refined simulation: Within each "sensitive area," a second round of virtual sensor meshes is deployed with smaller spacing (e.g., refining the mesh spacing to 0.1 meters). This step uses cluster analysis to focus on sensitive areas, helping to improve the coverage of the deployment.

[0025] Furthermore, the specific method for determining the final candidate points in step two is as follows: run the thermal runaway hydrogen leakage simulation again within the formed "sensitive area" and select 3 final candidate points within each "sensitive area".

[0026] Furthermore, the specific method for generating the final placement plan after global optimization in step three is as follows: Based on clustering results, the system recommends sensor installation locations using a multi-strategy fusion approach: The system first assesses the importance of each valid cluster. The importance score is determined by two factors: the cluster size (the number of sensor detection points within the cluster) and the spatial compactness of the cluster. Spatial compactness is evaluated by calculating the standard deviation of the coordinate values ​​of all points within the cluster on the X (long side of the container), Y (wide side of the container), and Z (height side of the container) axes. A smaller standard deviation indicates a more concentrated cluster and higher compactness. The system uses an inverse formula to calculate the compactness index: Compactness = 1 / (1 + mean standard deviation). The final importance score equals the cluster size multiplied by the compactness index; a larger product indicates a more important cluster. After calculation, the system sorts all clusters from highest to lowest importance score.

[0027] Core Region Center Recommendation: The system selects the cluster with the highest importance score as the primary leakage area. The recommended point location is directly taken as the geometric center of this cluster, that is, the average coordinates of all points within the cluster in the X, Y, and Z dimensions are calculated as the coordinates of the recommended point. This point is marked as the "core region center," and the description information includes the size of the cluster. Secondary region center recommendation: If the number of clusters is greater than or equal to 2 and the current number of recommended points has not reached the preset limit, the system will continue to select the cluster with the second highest importance score as the secondary leakage region. The location of the recommended point is also taken as the geometric center coordinates of the cluster. This point is marked as "secondary region center", and the description information includes the size value of the cluster; Strategic Coverage Point Recommendation: If the current number of recommended points has not yet reached the upper limit and there are at least two important clusters, the system performs strategic coverage point calculation. This strategy aims to optimize the monitoring network coverage. The system takes the coordinates of the center points of the two most important clusters, calculates the average coordinates of these two points in the X, Y, and Z dimensions, and uses the resulting midpoint as the recommended point. The cluster size of this point is the sum of the sizes of the two clusters, and the importance score is the arithmetic mean of the importance scores of the two clusters. This point is marked as a "strategic coverage point," and the description information indicates that it is a strategic location covering two main areas. Boundary expansion point recommendation: If the number of recommended points is still insufficient and at least one important cluster exists (containing multiple points), the system performs boundary expansion point calculation. This strategy aims to detect the direction of leak propagation in advance. The system first calculates the coordinate range (maximum value minus minimum value) of the main cluster along the X-axis. Then, based on the X-coordinate of the main cluster center point, it adds 40% of this range value outward to obtain the X-coordinate of the recommended point, while the Y and Z coordinates remain unchanged. The cluster size of this point is the same as the original cluster, and its importance score is 80% of the original cluster's importance score. This point is marked as a "boundary expansion point," and the description information indicates that it is the boundary expansion location of the main region. Supplementary Monitoring Point Recommendation: If the number of recommended points still does not reach the preset limit after the above strategies, the system will calculate supplementary monitoring points. This strategy generates supplementary points based on the already recommended points. If the existing point list is not empty, the system takes the coordinates of the first recommended point (usually the center point of the core area), adds 1.0 meter to its Y coordinate as the new Y coordinate of the recommended point, and keeps the X and Z coordinates unchanged. The cluster size of this point is the same as the base point, and its importance score is 60% of the base point's importance score. If there are no already recommended points (an extreme case), but clusters exist, the coordinates of the center point of the most important cluster are taken, and 1.0 meter is added to its X coordinate as the coordinates of the recommended point, with an importance score of 60% of the original cluster's importance score. This point is marked as a "supplementary monitoring point," and the description information indicates that it is a supplementary monitoring location.

[0028] The system ensures that the final number of recommended points does not exceed the preset maximum number of recommendations. If it does, the list is truncated, retaining only the first N points. Finally, the system outputs detailed information for all recommended points in sequence, including: serial number, type, X / Y / Z coordinates (rounded to two decimal places), and description.

[0029] In summary, this invention employs a simulation-based iterative optimization method for hydrogen sensor placement in energy storage containers, which solves the problems of unreasonable placement based on existing manual experience and low reliability of leak detection. This method achieves scientific and precise placement of hydrogen sensors, thereby improving the accuracy and safety of leak detection in energy storage cabinets.

[0030] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A simulation-based iterative optimization method for hydrogen sensor placement in energy storage containers, characterized in that: Includes the following steps: Step 1: Perform a global scan and identify potential detection points to obtain the most sensitive potential optimal deployment points for the leak source; Step two involves conducting localized, refined analysis and identifying candidate points to pinpoint "sensitive areas" and select the final candidate locations. Step 3: After the final candidate placement points are selected in Step 2, global optimization is performed, and the final placement plan is generated after the global optimization is completed. Step four: Use the final placement scheme generated in step three to perform simulation verification, and at the same time build a placement database, storing the verified optimal placement scheme into the placement database.

2. The method for optimizing the placement of hydrogen sensors in an energy storage container based on simulation iteration as described in claim 1, characterized in that: The specific method for performing a global scan in step one is as follows: First, a detailed 3D model is established: a 1:1 3D model of the energy storage container is established using fire dynamics software, including the internal structure such as battery racks, battery modules, ventilation openings, air conditioning system, and cable channels; Next, define the leakage scenarios: define several representative leakage scenarios, including: Leakage locations: battery pack connectors, module valves, and pipe connections; Leakage rate: Based on battery chemical reaction and safety valve data, different levels of leakage rate are set: small, medium, and large. Environmental conditions: Consider ventilation system on / off status and ambient temperature; Next, a "virtual sensor grid" is deployed: a large number of virtual sensors are evenly arranged at a high density inside the energy storage cabinet. Finally, simulation and data acquisition were performed: transient simulations were run for each leakage scenario, and hydrogen concentration-time series data were recorded at each virtual sensor location.

3. The method for optimizing the placement of hydrogen sensors in an energy storage container based on simulation iteration as described in claim 2, characterized in that: The specific method for identifying potential detection points in step one is as follows: Set a reliable detection concentration threshold, and for each leak scenario, identify the locations of several sensors that first exceed the threshold. These points are the potential optimal locations for the leak source that are "most sensitive".

4. The simulation-based iterative method for optimizing the placement of hydrogen sensors in an energy storage container, as described in any one of claims 1 to 3, is characterized in that: The specific method for performing localized refined analysis in step two is as follows: Constructing "sensitive areas": Spatial clustering analysis is performed on all the "potential detection points" identified in the first stage to form several key "sensitive areas"; Localized refined simulation: Within each "sensitive region", a second round of virtual sensor meshes is deployed at smaller intervals.

5. The method for optimizing the placement of hydrogen sensors in an energy storage container based on simulation iteration as described in claim 4, characterized in that: The specific method for determining the final candidate points in step two is as follows: Leakage simulations were run again within the formed "sensitive areas," taking into account not only potential detection points but also reliability, robustness, and concentration gradient. Based on these indicators, 1-2 final candidate detection points were selected within each "sensitive area." 6. The simulation-based iterative method for optimizing the placement of hydrogen sensors in an energy storage container, as described in any one of claims 1 to 3, is characterized in that: The specific method for generating the final deployment scheme after global optimization in step three is as follows: Based on clustering results, the system recommends sensor installation locations using a multi-strategy fusion approach: all final candidate locations are treated as a whole, taking into account the following constraints: a limit on the total number of sensors, coverage of all key leakage scenarios, and avoidance of redundancy. These constraints are input into an optimization algorithm, which optimizes the overall solution and selects an optimal subset from the candidate locations.

7. The method for optimizing the placement of hydrogen sensors in an energy storage container based on simulation iteration as described in claim 6, characterized in that: In step three, the overall scheme is optimized using an optimization algorithm. The specific method for selecting an optimal subset from the candidate points is as follows: First, the importance of each effective cluster is evaluated. The importance score is determined by two factors: one is the size of the cluster, i.e., the number of sensor detection points contained in the cluster; the other is the spatial compactness of the cluster. Spatial compactness is evaluated by calculating the standard deviation of the coordinate values ​​of all points in the cluster on the three coordinate axes of the X container's long side, Y container's wide side, and Z container's high side. The smaller the standard deviation, the more concentrated the points are, and the higher the compactness. The compactness index is calculated using an inverse proportional formula: compactness = 1 / (1 + average standard deviation). The final importance score is equal to the cluster size multiplied by the compactness index. The larger the product, the more important the cluster region is. After the calculation is completed, the system sorts all clusters from high to low importance scores. Core region center recommendation: The system selects the cluster with the highest importance score as the main leakage area. The recommended point location is directly taken as the geometric center of the cluster. That is, the average coordinates of all points in the cluster in the X, Y and Z dimensions are calculated as the coordinates of the recommended point. This point is marked as the "core region center". The description information includes the size value of the cluster. Secondary region center recommendation: If the number of clusters is greater than or equal to 2 and the number of current recommended points has not reached the preset limit, the system continues to select the cluster with the second highest importance score as the secondary leakage region. The recommended point location is also taken as the geometric center coordinate of the cluster. This point is marked as "secondary region center" and the description information includes the size value of the cluster. Strategic Coverage Point Recommendation: If the current number of recommended points has not yet reached the upper limit and there are at least two important clusters, the system performs strategic coverage point calculation. This strategy aims to optimize the coverage of the monitoring network. The system takes the coordinates of the center points of the two most important clusters and calculates the average coordinates of these two points in the X, Y, and Z dimensions. The resulting midpoint position is used as the recommended point. The cluster size of this point is the sum of the sizes of the two clusters, and the importance score is the arithmetic mean of the importance scores of the two clusters. This point is marked as a "strategic coverage point", and the description information indicates that it is a strategic location covering two main areas. Boundary expansion point recommendation: If the number of recommended points is still insufficient and there is at least one important cluster, the system performs boundary expansion point calculation. This strategy aims to detect the direction of leakage diffusion in advance. The system first calculates the coordinate range of the main cluster in the X-axis direction, and then adds 40% of this range value to the X-coordinate of the center point of the main cluster as the X-coordinate of the recommended point. The Y and Z coordinates remain unchanged. The cluster size of this point is the same as the original cluster, and the importance score is 80% of the importance score of the original cluster. This point is marked as a "boundary expansion point", and the description information indicates that it is the boundary expansion location of the main region. Supplementary monitoring point recommendation: If the number of recommended points still does not reach the preset limit after the above strategy, the system will perform supplementary monitoring point calculation. This strategy generates supplementary points based on the already recommended points. If the list of existing points is not empty, the system takes the coordinates of the first recommended point, adds 1.0 meter to its Y coordinate as the new Y coordinate of the recommended point, and keeps the X and Z coordinates unchanged. The cluster size of this point is the same as the base point, and the importance score is 60% of the importance score of the base point. If there are no already recommended points, but a cluster exists, the coordinates of the center point of the most important cluster are taken, and 1.0 meter is added to its X coordinate as the coordinates of the recommended point. The importance score is 60% of the original cluster importance score. This point is marked as a "supplementary monitoring point", and the description information indicates that it is a supplementary monitoring location. Finally, ensure that the final number of recommended points does not exceed the preset maximum number of recommended points. If it does, truncate the list and keep only the first N points. Output the detailed information of all recommended points in order, including: serial number, type, X / Y / Z coordinates and description information.