A real-time acquisition method and system for key indicators of fuel oil production

Abstract

The application discloses a kind of real-time acquisition method and system of key index of fuel oil production, it is related to data acquisition technical field, by determining all key indexes of fuel oil production, different index set is grouped based on index aggregation degree, establishes associated archive tree, establishes data lock window, each index set is set as data key, according to the whole data key of data lock window adaptation associated archive tree, set acquisition target in data lock window, establish the relationship grid of acquisition target and data key, finally the target information of acquisition target is searched along relationship grid, obtain all search points satisfying search condition, integrate the search data of each search point as the key index of fuel oil production.This application realizes hierarchical archiving according to process by index aggregation degree, avoids undifferentiated acquisition of total amount, reduces data redundancy, with the aid of data lock window and relationship grid, realize the dynamic accurate mapping of acquisition target and key index, improve the efficiency and accuracy of data acquisition.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, specifically to a method and system for real-time acquisition of key indicators in fuel oil production. Background Technology

[0002] The fuel oil production process involves many key indicators such as density, viscosity, flash point, and sulfur content. Traditional data collection methods typically collect all indicators indiscriminately without considering the differences in the correlation strength between different production processes and indicators, resulting in serious data redundancy and low collection efficiency. At the same time, the process dependencies between various processes are complex, and there are different degrees of coupling between key indicators.

[0003] Existing methods lack a hierarchical data acquisition architecture that can dynamically adapt to the characteristics of the process, making it difficult to accurately locate relevant indicators based on real-time data acquisition targets. This affects the timeliness and accuracy of key indicators. Furthermore, in production scenarios involving multiple batches and multiple processes, the mapping relationship between data acquisition targets and indicator sets is fixed and rigid, making it impossible to flexibly switch data acquisition views. This further restricts the real-time monitoring and optimization capabilities of the fuel oil production process. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for real-time acquisition of key indicators in fuel oil production, so as to solve the problems in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for real-time acquisition of key indicators in fuel oil production, comprising the following steps: Step S1: Determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of aggregation, and establish corresponding association archiving trees based on different indicator sets; Step S2: Establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relationship grid between the collection target and the data key; Step S3: Search along the relation grid to obtain all search points that meet the search conditions of the target, and integrate the search data of each search point as key indicators for fuel oil production.

[0006] In a preferred embodiment, the process of identifying all key indicators and grouping them into different indicator sets based on their aggregation degree, and then establishing an indicator association archive based on the different indicator sets, includes: Identify the major process categories in fuel oil production, define the aggregation degree of indicators for each major process category, obtain all key indicators, which include a number of indicator items, and determine the indicator items belonging to the corresponding major process category based on the aggregation degree of indicators for each major process category. All indicators within the same major process category are at a certain degree of aggregation. Grouping all indicators with the same degree of aggregation into a set of indicators determines the process dependency relationships between different major process categories, including strong dependency, general dependency, and weak dependency. Based on the process dependency relationship, an associated archiving tree is constructed for each indicator set, and the archiving relationship between different indicator sets is recorded through the associated archiving tree.

[0007] In a preferred embodiment, the associated archive tree includes: Create container nodes of type root, branch, and leaf. Container nodes of type root and branch are segmented and adjacent based on the index of type branch line. Container nodes of type branch and leaf are segmented and adjacent based on the index of type branch line. Grafting points are synchronously created on container nodes of type branch. Two sets of indicators with strong dependencies are stored in the two container nodes connected by the branch line, and two sets of indicators with general dependencies are stored in the two container nodes connected by the branch line. For each grafting point, the branch line and the branch line are synchronously connected to the grafting point, and the container nodes at both ends of the grafting point are in a weak dependency state. The corresponding index archiving relationship is identified by the branching line, branch line and grafting point. Container nodes of type root, branch and leaf, as well as the branching line, branch line and grafting point used to connect different container nodes, are merged into an associated archiving tree. The associated archive tree consists of an archive structure composed of different container nodes and index segments.

[0008] In a preferred embodiment, the process of establishing a data lock window includes: Initialize several window layers, determine one window layer as the window carousel interface, define several lock-key architectures to store several data keys, and arrange the data keys on the lock-key architectures based on the archive structure, with each arrangement position corresponding to an architecture point; Assign a corresponding lock-key architecture to each window layer, determine the call identifier of each window layer, store all call identifiers in the storage area of ​​the window carousel interface, select a call identifier, enable the corresponding window layer in the window carousel interface, lock the read permission of the storage area, and determine the currently enabled window layer as the data lock window.

[0009] In a preferred embodiment, the process of setting each indicator set as a data key and determining all data keys that are compatible with the data lock window based on the associated archive tree includes: The associated archive tree is used as the window to read the file of the data lock window. The data lock window adapts the corresponding archive structure from the file read by the window based on the lock-key architecture of the window layer, and reads the archive structure into the data lock window. All the data keys corresponding to the read archive structure are stored in the data lock window. Selecting the remaining call identifier switches the current window layer, obtains the next window layer of the data lock window, adapts the new archive structure based on the lock-key architecture of the switched window layer, and obtains all the data keys that each window layer adapts to in the data lock window.

[0010] In a preferred embodiment, the process of setting the corresponding acquisition target in the data lock window and establishing the relationship grid between the acquisition target and the data key includes: Identify the production processes for several batches, obtain the main key indicators for each batch of production processes, take the production processes of each batch as the data collection target, and set the main key indicators of each batch as the target information. Import all the data collection targets into the data lock window. The data lock window traverses its own window layers, selects the lock-key architecture that matches the data collection target for each window layer, and determines the architecture point of each data collection target on the lock-key architecture. Choose any one of the lock-key architecture points as the grid retrieval center, and use the architecture points other than the grid retrieval center as retrieval points. Each retrieval point corresponds to a data key that has an index archiving relationship with the grid retrieval center. Based on the index archiving relationship between each retrieval point and the grid retrieval center, the retrieval lines between each retrieval point and the grid retrieval center are determined. The two ends of the retrieval lines are associated with the container nodes at the two ends of the original index segment or grafting points. The container nodes are used as the retrieval source of the retrieval data corresponding to the retrieval lines. The retrieval points are connected to the grid retrieval center based on their respective retrieval lines. The retrieval lines are labeled with the index archiving relationship, and a corresponding relationship grid is constructed.

[0011] In a preferred embodiment, the adaptation of the lock-key architecture between the window layer and the acquisition target is based on: Locate the lock-key architecture of the current window layer that matches the root type architecture point in the archive structure, and determine whether the data key at the architecture point matches the main key indicators of the production process of the current batch. If so, determine the current window layer is compatible, identify the lock-key architecture that meets the conditions, associate the lock-key architecture with the acquisition target, and select the next window layer to obtain the lock-key architecture that is compatible with the acquisition target; If not, determine that there is no matching lock-key architecture in the current window layer, select the next window layer to perform the judgment, and continue until all lock-key architectures corresponding to each collection target are associated.

[0012] In a preferred embodiment, the process of retrieving target information corresponding to the target along a relational grid to obtain all retrieval points that meet the retrieval conditions for the target, and integrating the retrieval data from all retrieval points as key indicators for fuel oil production includes: The location of each target to be collected is determined as a grid point. Search conditions are set, including several sub-search items. The target information of the target to be collected is retrieved from the location of the grid point along each search line of the relation grid adjacent to the grid point. When a search point meets the search conditions for the target information, a pull net is pulled at the corresponding container node of the search point. The pull net is used to attach the container node to the pull net point. The pull net is marked with the sub-search entries corresponding to the container node and the pull net point. All the nets in the same sub-search item are grouped into a search subnet. When a matching sub-search item is input, all the container nodes connected by the matching search subnets are retrieved to obtain the search data for each container node. The search subnets of each sub-search entry included in the search criteria are integrated, and their respective search data are then integrated to serve as key indicators for a batch of fuel oil production for the current collection target. By integrating the key indicators of all batches, the final key indicators for fuel oil production are obtained.

[0013] This invention also provides a real-time acquisition system for key indicators of fuel oil production, the system comprising: The indicator archiving module is used to determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of indicator aggregation, and establish corresponding association archiving trees based on different indicator sets. The relational grid module is used to establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relational grid between the collection target and the data key. The grid retrieval module is used to retrieve target information corresponding to the collected target along the relation grid, obtain all retrieval points that meet the retrieval conditions of the collected target, and integrate the retrieval data of each retrieval point as key indicators for fuel oil production.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention groups key indicators of fuel oil production into different indicator sets according to the process and establishes an associated archiving tree to achieve hierarchical archiving based on process dependencies. This avoids the indiscriminate collection of all indicators, reduces data redundancy to a certain extent, and improves data collection efficiency.

[0015] 2. This invention establishes a data lock window, uses an indicator set as the data key, and dynamically adapts the key set under different selected window layers according to the associated archive tree. It supports flexible switching of multiple window layers, realizes on-demand adaptation under different acquisition views, and improves the adaptability of data acquisition.

[0016] 3. This invention constructs a grid relating the collection targets and data keys, uses the grid retrieval center as the grid point, determines the corresponding retrieval lines based on different index archiving relationships, and quickly filters all retrieval points that meet the retrieval conditions through the grid line and retrieval subnet mechanism, integrating them to obtain key indicators of fuel oil production, thus ensuring the comprehensiveness of key indicator collection. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of the associated archive tree in this invention.

[0020] Figure 3 This is a system block diagram of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, please refer to Figure 1 As shown in the figure, the real-time acquisition method for key indicators of fuel oil production described in this embodiment includes the following steps: Step S1: Determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of aggregation, and establish corresponding association archiving trees based on different indicator sets; Step S2: Establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relationship grid between the collection target and the data key; Step S3: Search along the relation grid to obtain all search points that meet the search conditions of the target, and integrate the search data of each search point as key indicators for fuel oil production.

[0023] It should be further explained that, in the specific implementation process, the process of identifying all key indicators corresponding to fuel oil production and preparation, grouping all key indicators into different indicator sets based on their aggregation degree, and establishing an associated archiving tree based on the different indicator sets includes: Based on the process operations corresponding to fuel oil production and preparation, a number of process categories are determined for fuel oil production, and the aggregation degree of indicators corresponding to each process category is defined. The aggregation degree of indicators is used to aggregate all key indicators belonging to the corresponding process category. Among them, the index aggregation degree is a hierarchical aggregation constraint parameter set based on the process attributes, reaction mechanism, working environment and quality correlation strength of each major category of fuel oil production process. It is used to quantify the degree of correlation between a single process and various key indicators, thereby clustering, dividing and grouping all key fuel oil indicators to automatically form several data sets corresponding to processes with clear boundaries and coupling relationships. Obtain all key indicators corresponding to the completion of fuel oil production and preparation. All key indicators include a number of indicator items. Based on the degree of aggregation of indicators corresponding to each major process category, determine the indicator items belonging to different major process categories. All indicators under the same major process category are identified as belonging to a certain degree of aggregation. All indicators under the same degree of aggregation are grouped to obtain corresponding indicator sets. Each indicator set is used to characterize the key indicators corresponding to a specific sub-category of process operation under a major process category. All key indicators include, but are not limited to, fuel oil density, viscosity, flash point, sulfur content, pour point, residual carbon, ash content, acid value, calorific value, impurity content, and corrosiveness indicators.

[0024] It should be noted that by pre-configuring corresponding gradient index aggregation degrees for each major category of fuel oil production processes, the index aggregation degree is used to characterize the correlation and coupling strength between different production processes and key indicators of fuel oil production. Based on the differences in index aggregation degrees, all key indicator items are hierarchically clustered and grouped to obtain multiple sets of indexes with matching correlations. This allows different processes to be matched with dedicated index collection groups, avoiding the indiscriminate collection of all indicators, reducing data redundancy, and achieving efficient real-time collection by partition and level.

[0025] The process dependencies between different process categories are determined, including strong dependency, general dependency, and weak dependency. Based on different process dependencies, correlation coefficients are obtained between different indicator sets. The correlation coefficients for the indicator sets corresponding to strong dependency, general dependency, and weak dependency decrease sequentially, with a value range of (0, 1). The closer the value is to 1, the stronger the dependency between the two process categories and the higher the degree of connection between their key indicators. The closer the value is to 0, the weaker the dependency between the two process categories and the lower the degree of connection between their key indicators. The value ranges of the correlation coefficients corresponding to strong dependency, general dependency, and weak dependency are as follows: strong dependency ≥ 0.7, 0.4 ≤ general dependency < 0.7, and weak dependency < 0.4.

[0026] Based on the process dependency relationship, a separate association archive tree is constructed for each indicator set. The association archive tree records the indicator archive relationship between different indicator sets, as follows: Container nodes of type root, branch, and leaf are established respectively. Container nodes of type root and branch are adjacent to each other based on the index of type branch line. Container nodes of type branch and leaf are adjacent to each other based on the index of type branch line. At the location of container node of type branch, the corresponding grafting point is established synchronously. At the two container nodes connected by the branch line, two sets of indicators with strong dependencies are stored in tree order, and strong dependencies are marked on the corresponding branch line. At the two container nodes connected by the branch line, two sets of indicators with general dependencies are stored in tree order, and general dependencies are marked on the corresponding branch line. The direction between a root container node and a branch or leaf container node is set to tree order, and the direction between a branch container node and a leaf container node is set to tree order; this symbolizes that data is passed from the root of the tree to the branches or leaves. For the branching lines and trunk lines connected to the container nodes of type bifurcation, the grafting points are processed. The branching lines and trunk lines are synchronously connected to the grafting points. Weak dependencies are marked at the grafting points to indicate that the container nodes connected at both ends of the grafting point are weakly dependent. The container nodes, including those of type root, branch, and leaf, and the branching lines, trunk lines, and grafting points used to connect the different container nodes, are merged into an associated archive tree. The identifiers corresponding to the branching lines, trunk lines, and grafting points are used as the archive identifiers corresponding to the container nodes at their respective ends. The archive identifiers are used to record the index archive relationships between the index sets represented by the corresponding container nodes; that is, the index archive relationships are any one of strong dependency, general dependency, and weak dependency. The associated archive tree consists of a number of container nodes and index segments. Each index segment is adjacent to a container node at both ends. Different index segments are adjacent to each other through container nodes, forming an archive structure of the corresponding associated archive tree.

[0027] Please refer to the structural composition of the associated archive tree. Figure 2 As shown; It should be noted that container nodes and index segments are both virtual operation objects registered on the virtual machine. Each virtual operation object is allocated corresponding memory space on the virtual machine to store its corresponding processed data. The virtual operation objects perform operations in the virtual machine based on the allocated memory address.

[0028] It should be further explained that, in the specific implementation process, the process of establishing a data lock window, setting each indicator set as a data key, and determining all data keys that are compatible with the data lock window based on the associated archive tree includes: Initialize a number of window layers, determine one of the window layers as the window carousel interface, define a number of lock-key architectures, each lock-key architecture is used to store a number of data keys, the data keys are arranged on the lock-key architecture based on the archive structure of the associated archive tree, and each arrangement position corresponds to an architecture point of the lock-key architecture. Assign a corresponding lock-key architecture to each window layer, and merge the layer number corresponding to each window layer with the architecture number of their respective lock-key architecture as their respective call identifiers. Store all call identifiers in the storage area of ​​the window carousel interface. Select a call identifier, enable the corresponding window layer to the window carousel interface based on the call identifier, lock the read permission of the storage area, and determine the window carousel interface with the currently enabled unique window layer as the corresponding data lock window; The associated archive tree is used as the window reading file corresponding to the data lock window. The data lock window adapts the corresponding archive structure from the window reading file based on the lock-key architecture of the window layer it calls, and reads the archive structure into the data lock window. All the data keys corresponding to the currently read archive structure are stored in the data lock window. Repeatedly select the remaining call identifiers to switch the current window layer, obtain the next window layer of the data lock window, and adapt the new archive structure corresponding to the associated archive tree based on the lock-key architecture of the switched window layer, thereby obtaining all the data keys corresponding to each window layer in the data lock window.

[0029] It should be further explained that, in the specific implementation process, the process of setting the corresponding acquisition target in the data lock window and establishing the relationship grid between the acquisition target and the data key includes: The production process corresponding to several batches of fuel oil production and preparation is determined, the main key indicators corresponding to the production process of each batch are obtained, the production process of each batch of fuel oil production and preparation is determined as the data collection target, and the main key indicators of the production process of each batch are set as their respective target information. Each batch's production process corresponding to its batch sequence number is used as its respective retrieval credential. Import all the data collection targets into the data lock window. The data lock window then iterates through each of its corresponding window layers and selects the lock-key architecture that matches the data collection target stored in each window layer. The adaptation is based on the following: The lock-key architecture of the current window layer matches the architecture point of type root in the archive structure. It is then determined whether the data key stored at the architecture point matches the main key indicators of the current batch's production process. If so, the current window layer is deemed compatible, the matching lock-key architecture is identified, and the lock-key architecture is associated with the acquisition target. The next window layer is then selected to obtain a lock-key architecture compatible with the acquisition target. If not, the current window layer does not have a matching lock-key architecture, and the next window layer is directly selected for further analysis. This process continues until all lock-key architectures corresponding to each acquisition target are associated.

[0030] Determine the architecture point corresponding to each collection target on each lock-key architecture, select any architecture point of the lock-key architecture as the grid retrieval center, and determine the architecture points on each lock-key architecture other than the grid retrieval center as retrieval points; Each retrieval point corresponds to a data key (indicator set) that has an indicator archiving relationship with the grid retrieval center. Based on the indicator archiving relationship between each retrieval point and the grid retrieval center, the retrieval line connecting each retrieval point and the grid retrieval center is determined. The specific details of determining the retrieval lines connecting each retrieval point to the grid retrieval center are as follows: When the retrieval point and the grid retrieval center have a strong dependency relationship in index archiving, the index segment of type slit line is determined as the retrieval line between the retrieval point and the grid retrieval center. When the retrieval point and the grid retrieval center are in a general dependent index archiving relationship, the index segment of type branch line is determined as the retrieval line between the retrieval point and the grid retrieval center. When the retrieval point and the grid retrieval center have a weak dependency relationship in index archiving, the retrieval line between the retrieval point and the grid retrieval center is determined by connecting the branching line and the trunk line at both ends of the container node at the synchronization position through the grafting point. The two ends of the retrieval line are associated with the container nodes at the two ends of the original index segment or grafting point. The container nodes are used as the retrieval source of the retrieval data (data key) corresponding to the retrieval line. All retrieval points are connected to the grid retrieval center based on their respective retrieval lines. The index archiving relationship is marked on the retrieval line, thereby completing the corresponding relation grid.

[0031] It should be noted that each retrieval point and retrieval line corresponds to a virtual operation object registered on the virtual machine. The retrieval point is allocated a single sequence of memory addresses on the virtual machine, while the retrieval line is allocated a series of memory addresses on the virtual machine. The memory addresses at the beginning and end of the retrieval line correspond to the memory addresses of the respective retrieval points. The virtual operation objects perform operations on the virtual machine based on the allocated memory addresses.

[0032] It should be further explained that, in the specific implementation process, the process of retrieving the target information corresponding to the collection target along the relational grid to obtain all retrieval points that meet the retrieval conditions of the collection target, and integrating the retrieval data of each of the retrieval points as key indicators for fuel oil production includes: For the structural composition relationship of the relational grid, please refer to [link / reference]. Figure 3 As shown; The location of each data collection target in the relational grid (grid retrieval center) is determined as the grid point. Retrieval conditions are set, including several sub-retrieval entries. The sub-retrieval entries are container nodes that are in a certain indicator archiving relationship with the grid retrieval center where the main key indicators (target information) of the production process of the current data collection target are located. Among them, the index archiving relationship between the pull network point and the container node is a set of relationships consisting of strong dependency, general dependency and weak dependency. The number of index archiving relationships in the set of relationships is determined by the retrieval party based on its own retrieval needs. The target information corresponding to the target is retrieved from the location of the grid point and along each retrieval line in the relation grid directly adjacent to the grid point. When the retrieval point meets the retrieval conditions corresponding to the target information, a grid line is pulled at the corresponding container node of the retrieval point. The mesh line is used to attach container nodes to mesh points. The mesh line is marked with the corresponding sub-search entries between the container node and the mesh point. All mesh lines in the same sub-search entry are combined into a search subnet. When a matching sub-search entry is input, all container nodes connected by the matching search subnets are searched to obtain the search data corresponding to each container node. The search subnets obtained by each sub-search entry corresponding to the search conditions are integrated, and then the search data corresponding to each of the search subnets are integrated as the key indicators of a batch of fuel oil production corresponding to the current collection target. The key indicators of all batches are integrated to obtain the final key indicators of fuel oil production.

[0033] Example 2, please refer to Figure 3 As shown in this embodiment, a real-time acquisition system for key indicators of fuel oil production is described. This system includes: The indicator archiving module is used to determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of indicator aggregation, and establish corresponding association archiving trees based on different indicator sets. The relational grid module is used to establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relational grid between the collection target and the data key. The grid retrieval module is used to retrieve target information corresponding to the collected target along the relation grid, obtain all retrieval points that meet the retrieval conditions of the collected target, and integrate the retrieval data of each retrieval point as key indicators for fuel oil production.

[0034] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for real-time acquisition of key indicators in fuel oil production, characterized in that, Includes the following steps: Step S1: Determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of aggregation, and establish corresponding association archiving trees based on different indicator sets; Step S2: Establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relationship grid between the collection target and the data key; Step S3: Search along the relation grid to obtain all search points that meet the search conditions of the target, and integrate the search data of each search point as key indicators for fuel oil production.

2. The method for real-time acquisition of key indicators in fuel oil production according to claim 1, characterized in that, The process of identifying all key metrics, grouping them into different metric sets based on their aggregation degree, and establishing metric associations and archives based on these different metric sets includes: Identify the major process categories in fuel oil production, define the aggregation degree of indicators for each major process category, obtain all key indicators, which include a number of indicator items, and determine the indicator items belonging to the corresponding major process category based on the aggregation degree of indicators for each major process category. All indicators within the same major process category are at a certain degree of aggregation. Grouping all indicators with the same degree of aggregation into a set of indicators determines the process dependency relationships between different major process categories, including strong dependency, general dependency, and weak dependency. Based on the process dependency relationship, an associated archiving tree is constructed for each indicator set, and the archiving relationship between different indicator sets is recorded through the associated archiving tree.

3. The method for real-time acquisition of key indicators in fuel oil production according to claim 2, characterized in that, The associated archive tree includes: Create container nodes of type root, branch, and leaf. Container nodes of type root and branch are segmented and adjacent based on the index of type branch line. Container nodes of type branch and leaf are segmented and adjacent based on the index of type branch line. Grafting points are synchronously created on container nodes of type branch. Two sets of indicators with strong dependencies are stored in the two container nodes connected by the branch line, and two sets of indicators with general dependencies are stored in the two container nodes connected by the branch line. For each grafting point, the branch line and the branch line are synchronously connected to the grafting point, and the container nodes at both ends of the grafting point are in a weak dependency state. The corresponding index archiving relationship is identified by the branching line, branch line and grafting point. Container nodes of type root, branch and leaf, as well as the branching line, branch line and grafting point used to connect different container nodes, are merged into an associated archiving tree. The associated archive tree consists of an archive structure composed of different container nodes and index segments.

4. The method for real-time acquisition of key indicators in fuel oil production according to claim 3, characterized in that, The process of creating a data lock window includes: Initialize several window layers, determine one window layer as the window carousel interface, define several lock-key architectures to store several data keys, and arrange the data keys on the lock-key architectures based on the archive structure, with each arrangement position corresponding to an architecture point; Assign a corresponding lock-key architecture to each window layer, determine the call identifier of each window layer, store all call identifiers in the storage area of ​​the window carousel interface, select a call identifier, enable the corresponding window layer in the window carousel interface, lock the read permission of the storage area, and determine the currently enabled window layer as the data lock window.

5. The method for real-time acquisition of key indicators in fuel oil production according to claim 4, characterized in that, The process of setting each set of metrics as a data key and determining all data keys that are compatible with the data lock window based on the associated archive tree includes: The associated archive tree is used as the window to read the file of the data lock window. The data lock window adapts the corresponding archive structure from the file read by the window based on the lock-key architecture of the window layer, and reads the archive structure into the data lock window. All the data keys corresponding to the read archive structure are stored in the data lock window. Selecting the remaining call identifier switches the current window layer, obtains the next window layer of the data lock window, adapts the new archive structure based on the lock-key architecture of the switched window layer, and obtains all the data keys that each window layer adapts to in the data lock window.

6. The method for real-time acquisition of key indicators in fuel oil production according to claim 5, characterized in that, The process of setting the corresponding acquisition target in the data lock window and establishing the relationship grid between the acquisition target and the data key includes: Identify the production processes for several batches, obtain the main key indicators for each batch of production processes, take the production processes of each batch as the data collection target, and set the main key indicators of each batch as the target information. Import all the data collection targets into the data lock window. The data lock window traverses its own window layers, selects the lock-key architecture that matches the data collection target for each window layer, and determines the architecture point of each data collection target on the lock-key architecture. Choose any one of the lock-key architecture points as the grid retrieval center, and use the architecture points other than the grid retrieval center as retrieval points. Each retrieval point corresponds to a data key that has an index archiving relationship with the grid retrieval center. Based on the index archiving relationship between each retrieval point and the grid retrieval center, the retrieval lines between each retrieval point and the grid retrieval center are determined. The two ends of the retrieval lines are associated with the container nodes at the two ends of the original index segment or grafting points. The container nodes are used as the retrieval source of the retrieval data corresponding to the retrieval lines. The retrieval points are connected to the grid retrieval center based on their respective retrieval lines. The retrieval lines are labeled with the index archiving relationship, and a corresponding relationship grid is constructed.

7. The method for real-time acquisition of key indicators in fuel oil production according to claim 6, characterized in that, The adaptation criteria for the lock-key architecture between the window layer and the acquisition target are as follows: Locate the lock-key architecture of the current window layer that matches the root type architecture point in the archive structure, and determine whether the data key at the architecture point matches the main key indicators of the production process of the current batch. If so, determine the current window layer is compatible, identify the lock-key architecture that meets the conditions, associate the lock-key architecture with the acquisition target, and select the next window layer to obtain the lock-key architecture that is compatible with the acquisition target; If not, determine that there is no matching lock-key architecture in the current window layer, select the next window layer to perform the judgment, and continue until all lock-key architectures corresponding to each collection target are associated.

8. The method for real-time acquisition of key indicators in fuel oil production according to claim 7, characterized in that, The process of retrieving target information corresponding to the target along the relation grid to obtain all retrieval points that meet the retrieval conditions of the target, and integrating the retrieval data from all retrieval points as key indicators for fuel oil production includes: The location of each target to be collected is determined as a grid point. Search conditions are set, including several sub-search items. The target information of the target to be collected is retrieved from the location of the grid point along each search line of the relation grid adjacent to the grid point. When a search point meets the search conditions for the target information, a pull net is pulled at the corresponding container node of the search point. The pull net is used to attach the container node to the pull net point. The pull net is marked with the sub-search entries corresponding to the container node and the pull net point. All the nets in the same sub-search item are grouped into a search subnet. When a matching sub-search item is input, all the container nodes connected by the matching search subnets are retrieved to obtain the search data for each container node. The search subnets of each sub-search entry included in the search criteria are integrated, and their respective search data are then integrated to serve as key indicators for a batch of fuel oil production for the current collection target. By integrating the key indicators of all batches, the final key indicators for fuel oil production are obtained.

9. A real-time acquisition system for key indicators in fuel oil production, used to implement the real-time acquisition method for key indicators as described in any one of claims 1-8, characterized in that, The system includes: The indicator archiving module is used to determine all key indicators corresponding to fuel oil production and preparation, group all key indicators into different indicator sets based on the degree of indicator aggregation, and establish corresponding association archiving trees based on different indicator sets. The relational grid module is used to establish a data lock window, set each indicator set as a data key, determine all data keys that are compatible with the data lock window based on the associated archive tree, set the corresponding collection target in the data lock window, and establish a relational grid between the collection target and the data key. The grid retrieval module is used to retrieve target information corresponding to the collected target along the relation grid, obtain all retrieval points that meet the retrieval conditions of the collected target, and integrate the retrieval data of each retrieval point as key indicators for fuel oil production.

Images