Full-dimensional digital operation decision-making method and system and storage medium

Abstract

The invention relates to the field of digital information processing, in particular to a full-dimension digital operation decision-making method and system.The full-dimension data sets of all user sides are collected and divided into a plurality of data subsets corresponding to different attributes, and all the data subsets are stored in subordinate container units of a data storage array; an application program set of the cloud platform is in butt joint with the data storage array, matching task processing of the data subsets is achieved, and a task processing result is returned to the container unit; loading the container unit to a modeling engine to realize modeling operation of the modeling engine; and a modeling result of the modeling engine is returned to an operation decision task thread of the user side, so that a control instruction is sent to a subordinate device side of the user side, and reliable decision guidance is provided for determining a working state control strategy of the device side. According to the invention, the operation efficiency of the equipment ends and the operation collaboration among different equipment ends can be improved, the overall operation efficiency of the park is improved, and the accident occurrence probability of the park is reduced.

Description

Technical Field

[0001] The present invention relates to the field of digital information processing, and in particular to a full-dimensional digital operation decision-making method, system and storage medium. Background Art

[0002] Smart industrial parks connect a large number of user terminals and device terminals to the cloud platform through the Internet of Things. The user terminals can be terminals such as smartphones or computers held by staff within the park, and the device terminals can be sensor devices distributed in different locations within the park and production equipment that performs different production processes. As the controller of specific production processes within the park, the user terminal can directly control the corresponding production equipment to achieve efficient and accurate operation of the production equipment.

[0003] The main function of a smart industrial park is to coordinate the management and control of all production equipment within the park. The relationship between the user end and the equipment end in the existing park is limited to one-to-one management and control, that is, the user end can only control the operation of the equipment end based on a single data source. The above method cannot guarantee that the equipment end can use the complex and changeable environment in the park. For example, when the power supply in the park is unstable and / or there are multiple fire hazards, the user end will not be able to accurately adjust the working status of the equipment end based on the multi-dimensional data on power supply and fire hazards in the park, and will not be able to provide accurate and reliable operation decision-making solutions for the efficient operation of the equipment end in the park and the coordinated operation between different equipment ends. It can be seen that how to use the full-dimensional data in the park to achieve reliable management of the equipment end by the user end, the efficient operation of the equipment end itself, and the coordinated operation between different equipment ends is of great significance to maintaining the overall operating efficiency of the park and reducing the probability of production accidents in the park. Summary of the invention

[0004] In order to avoid using a single data to manage the operation decision of the equipment end, thereby affecting the operation efficiency of the equipment end and the operation coordination between different equipment ends, and to improve the overall operation efficiency of the park and reduce the probability of accidents in the park, the present invention provides a full-dimensional digital operation decision method, which includes the following steps: Monitor all user terminals in the park, collect full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; based on the storage state characteristics of the data storage array, store all data subsets in the corresponding container units under the data storage array; Connecting the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; returning the matching task processing result to the corresponding container unit based on the credibility of the matching task processing result; Based on the modeling requirement characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed; Based on the running status characteristics of the user terminal, the modeling result of the modeling engine is returned to the operation decision task thread of the user terminal; based on the execution status characteristics of the operation decision task thread, a control instruction is sent to the device terminal under the user terminal.

[0005] Preferably, all user terminals in the park are monitored, and the full-dimensional data sets of all user terminals are collected. Based on the data attributes of the full-dimensional data set, the full-dimensional data set is divided into a number of data subsets; based on the storage state characteristics of the data storage array, all data subsets are stored in the corresponding container units under the data storage array, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, so as to divide the full-dimensional data set into a number of data subsets; Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units in the data storage array that are in an available state; based on the data type compatibility characteristics and storage capacity of all container units in an available state, store all data subsets in corresponding container units that are in an available state.

[0006] Preferably, the application set of the cloud platform is connected to the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform, determine the data processing permission information of all applications, and integrate the applications that meet the preset data processing permission requirements into an application set; based on the gateway status of the network where the cloud platform is located, connect the application set with the data storage array; and based on the running threads of all applications under the application set, perform matching task processing on the data subset; Obtaining thread running error occurrence parameters of the matching task processing, and judging whether the matching task processing result is credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

[0007] Preferably, based on the modeling requirement characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed, specifically: The knowledge graph of the modeling engine is parsed to determine the metadata type features required by the modeling engine in the current modeling process, and the features are used as the modeling requirement features; the metadata type features are compared with the container units storing the matching processing results in the data storage array to determine the container units matching the current modeling process, and the mirror image of the matching container unit is loaded into the modeling engine; The modeling process of the modeling engine is monitored to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, the storage content of the used matching container units is cleared.

[0008] Preferably, based on the running state characteristics of the user terminal, the modeling result of the modeling engine is returned to the operation decision task thread of the user terminal; based on the execution state characteristics of the operation decision task thread, a control instruction is sent to the device terminal under the user terminal, specifically: Parsing and processing the operation decision operation instruction received by the user terminal to obtain the operation decision operation environment state characteristics of the user terminal, thereby judging whether the user terminal meets the preset operation decision operation environment conditions; if the preset operation decision operation environment conditions are met, returning the modeling result of the modeling engine to the operation decision task thread of the user terminal; Based on the execution progress status characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted; based on the complete operation decision result, a work behavior control instruction is sent to the device terminal under the user terminal.

[0009] On the other hand, the present invention provides a full-dimensional digital operation decision-making system, which includes the following modules: A data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; A data storage control module, used for storing all data subsets in corresponding container units under the data storage array based on the storage state characteristics of the data storage array; A data processing control module is used to connect the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility status of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit; A modeling engine control module, used for selecting a matching container unit from the data storage array and loading it into the modeling engine based on the modeling requirement characteristics of the modeling engine; A container change processing module, used for performing change processing on the matched container unit based on the modeling state characteristics of the modeling engine; An operation decision execution module, used for returning the modeling result of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal; The instruction sending module is used to send control instructions to the device terminal under the user terminal based on the execution state characteristics of the operation decision task thread.

[0010] Preferably, the data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, so as to divide the full-dimensional data set into a number of data subsets; The data storage control module is used to store all data subsets in corresponding container units under the data storage array based on the storage state characteristics of the data storage array, specifically: Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units in the data storage array that are in an available state; based on the data type compatibility characteristics and storage capacity of all container units in an available state, store all data subsets in corresponding container units that are in an available state.

[0011] Preferably, the data processing control module is used to connect the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility status of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform, determine the data processing permission information of all applications, and integrate the applications that meet the preset data processing permission requirements into an application set; based on the gateway status of the network where the cloud platform is located, connect the application set with the data storage array; and based on the running threads of all applications under the application set, perform matching task processing on the data subset; Obtaining thread running error occurrence parameters of the matching task processing, and judging whether the matching task processing result is credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

[0012] Preferably, the modeling engine control module is used to select matching container units from the data storage array and load them into the modeling engine based on the modeling requirement characteristics of the modeling engine, specifically: The knowledge graph of the modeling engine is parsed to determine the metadata type features required by the modeling engine in the current modeling process, and the features are used as the modeling requirement features; the metadata type features are compared with the container units storing the matching processing results in the data storage array to determine the container units matching the current modeling process, and the mirror image of the matching container unit is loaded into the modeling engine; The container change processing module is used to perform change processing on the matched container unit based on the modeling state characteristics of the modeling engine, specifically: Monitor the modeling process of the modeling engine to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, clear the storage content of the used matching container units; The operation decision execution module is used to return the modeling result of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal, specifically: Parsing and processing the operation decision operation instruction received by the user terminal to obtain the operation decision operation environment state characteristics of the user terminal, thereby judging whether the user terminal meets the preset operation decision operation environment conditions; if the preset operation decision operation environment conditions are met, returning the modeling result of the modeling engine to the operation decision task thread of the user terminal; The instruction issuing module is used to send a control instruction to the device terminal under the user terminal based on the execution state characteristics of the operation decision task thread, specifically: Based on the execution progress status characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted; based on the complete operation decision result, a work behavior control instruction is sent to the device terminal under the user terminal.

[0013] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements any of the above methods when executed by a processor.

[0014] Compared with the prior art, the present invention has the following beneficial effects: All user terminals in the industrial park are monitored, and the full-dimensional data sets of all user terminals are collected. Based on the data attributes of the full-dimensional data set, the full-dimensional data set is divided into several data subsets; based on the storage status characteristics of the data storage array, all data subsets are stored in the corresponding container units under the data storage array. Each production operation area in the industrial park will be equipped with a user terminal, which can be, but not limited to, a terminal such as a smartphone or computer held by the staff responsible for the operation area, which can actively or passively collect the full-dimensional data set corresponding to the production operation area; among them, the full-dimensional data can be, but not limited to, the water, electricity and gas supply data of the production operation area, the operation status data of the production equipment, the logistics distribution data of the production raw materials, the on-the-job status data of the employees, etc. These full-dimensional data can characterize the production process status in different aspects such as the production environment, production materials, and human resources of the industrial park. From the above introduction to the full-dimensional data set, we can see that the full-dimensional data set contains data on different types of attributes of the industrial park. In order to avoid data crosstalk in the subsequent application processing of the full-dimensional data, it is necessary to distinguish and classify the full-dimensional data set in advance, and divide it into several data subsets based on the attributes of each data item under the full-dimensional data set to avoid each data subset from mixing with other data of different categories of attributes and affecting the credibility of the task processing results of the application. In addition, the communication bandwidth of the user end itself is limited. If the cloud platform directly retrieves the data subset from the user end for application processing, it will cause a large communication load on the user end, affecting the normal work of the user end and even causing the user end to crash. For this reason, the data storage array is used as the data storage transfer node between the user end and the cloud platform, and different data subsets are stored separately through all container units in the data storage array to avoid crosstalk between different data subsets during the storage process and improve the storage independence and security of the data subsets.

[0015] The application set of the cloud platform is docked with the data storage array to perform matching task processing on the data subset; based on the trustworthy status of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit. The cloud platform has higher computing power and memory resource support. Compared with the user end, it can run multiple applications stably at the same time to realize the parallel operation of multiple applications. However, the data sources required in the task processing process of different applications are not the same. In order to accurately provide data sources for the task processing of all applications, the application set of the cloud platform is docked with the data storage array. Each application under the application set can obtain a suitable data subset for processing matching tasks to ensure the correctness of the task processing of the application. In addition, thread running errors are inevitable in the process of application processing tasks, resulting in errors in the running results of the corresponding threads, and they are continuously accumulated during the thread running process, thereby affecting the credibility of the final task processing results. The modeling operation of the modeling engine is realized based on the task processing results of all applications in the cloud platform. In order to ensure the correct modeling of the modeling engine, it is necessary to determine whether the matching task processing results of the application are credible, and return the credible matching task processing results to the container unit where the corresponding data subset is located, so that the modeling engine can accurately retrieve the required matching task processing results at any time for modeling operations.

[0016] Based on the modeling demand characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed. The modeling engine can be, but is not limited to, an engine that uses the computing power and memory resources of the cloud platform to perform modeling operations, so that the modeling engine does not need to have its own resource library, thereby realizing the lightweight of the modeling engine. The modeling operation of the modeling engine is implemented according to the corresponding knowledge graph, and the knowledge graph represents the process of the modeling operation. In order to provide a reliable modeling data source for the entire modeling operation of the modeling engine, it is necessary to first determine the modeling demand characteristics of the modeling engine, and compare them with the matching processing results stored in all container units in the data storage array to determine the required data source corresponding to each modeling process of the modeling engine, so as to accurately load the container unit into the modeling engine, so that the modeling engine can directly and accurately retrieve data from the container unit for modeling. In addition, when the modeling engine completes a certain modeling process and does not need to continue to retrieve the data in the container unit, the container unit can be changed at this time to avoid the container unit being occupied for a long time and improve the recycling efficiency of the container unit.

[0017] Based on the operation status characteristics of the user end, the modeling results of the modeling engine are returned to the operation decision task thread of the user end; based on the execution status characteristics of the operation decision task thread, control instructions are sent to the device end under the user end. The user end is directly controlled and connected to the device end used to execute the production process, so that the user end can adjust the operation status of the device end individually and coordinate the operation relationship between different device ends. In order to improve the control and adjustment performance of the user end to the device end, the modeling results generated by the modeling engine are used to guide the control strategy of the user end to the device end. To this end, based on the operation decision state of the device end selected by the user end to execute different control strategies, the corresponding modeling result is selected from the modeling engine and returned to the operation decision task thread of the user end. When the user end completes the operation decision task thread, based on the complete operation decision result, the user end is instructed to send work behavior control instructions to the subordinate device end, so that the device end can adjust its own working state, so as to match the environmental conditions of the park and / or coordinate with other device ends in the work process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 It is a flow chart of a full-dimensional digital operation decision-making method provided by the present invention.

[0019] Figure 2 It is a structural diagram of a full-dimensional digital operation decision-making system provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention rather than all structures are shown in the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] The terms "including" and "having" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] See also Figure 1 As shown, the present invention provides a full-dimensional digital operation decision-making method, which includes the following steps: S100, monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; based on the storage state characteristics of the data storage array, store all data subsets in the corresponding container units under the data storage array.

[0024] Furthermore, all user terminals in the park are monitored, and the full-dimensional data sets of all user terminals are collected. Based on the data attributes of the full-dimensional data set, the full-dimensional data set is divided into several data subsets; based on the storage state characteristics of the data storage array, all data subsets are stored in the corresponding container units under the data storage array, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, thereby dividing the full-dimensional data set into several data subsets; Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units in the data storage array that are in an available state; based on the data type compatibility characteristics and storage capacity of all container units in an available state, store all data subsets in corresponding container units that are in an available state.

[0025] Each production operation area in the industrial park will be equipped with a user terminal, which can be, but not limited to, a terminal such as a smart phone or computer held by the staff in charge of the operation area, or a server terminal responsible for all equipment ends in the operation area, which can actively or passively collect the full-dimensional data set corresponding to the production operation area, that is, the user terminal can actively collect the corresponding operation data from the water, electricity, gas lines and equipment ends corresponding to the operation area, or regularly receive the operation data fed back by the water, electricity, gas lines and equipment ends corresponding to the operation area, so as to integrate all the operation data into a full-dimensional data set; wherein, the full-dimensional data can be, but not limited to, the water, electricity, gas supply data of the production operation area, the operation status data of the production equipment, the logistics distribution data of the production raw materials, the on-the-job status data of the employees, etc. These full-dimensional data can characterize the production process status in different aspects such as the production environment, production materials, and human resources of the industrial park. The user terminal actively or passively collects the full-dimensional data set through the software tools installed by itself, and the working status of the software tools directly affects the reliability of the collected full-dimensional data. When the software tool works abnormally, the collected full-dimensional data will contain a lot of interference noise. First, based on the work logs of all user terminals, determine the software tools in normal working state in each user terminal, and then only monitor and extract the full-dimensional data set for the software tools that work normally. Also based on the interface bandwidth of the software tools that work normally on the user terminal, determine the parameters such as the monitoring operation frequency of the software tools that work normally. Generally speaking, the larger the interface bandwidth of the software tool, the greater the monitoring operation frequency of the software tool. Make full use of the interface bandwidth of the interface tool to improve the extraction speed of the full-dimensional data set of the software tool. From the above introduction to the full-dimensional data set, it can be seen that the full-dimensional data set contains data on different types of attributes of the industrial park. In order to avoid data crosstalk in the subsequent application processing of the full-dimensional data, it is necessary to distinguish and classify the full-dimensional data set in advance, and divide it into several data subsets based on the attributes of each data item under the full-dimensional data set to avoid each data subset from mixing with other data of different categories of attributes and affecting the credibility of the task processing results of the application.

[0026] In addition, the communication bandwidth of the user end itself is limited. If the cloud platform directly retrieves the data subset from the user end for application processing, it will cause a large communication load on the user end, affecting the normal operation of the user end and even causing the user end to crash. For this reason, the data storage array is used as a data storage transfer node between the user end and the cloud platform, and different data subsets are stored separately through all container units (i.e., independent storage units) in the data storage array to avoid crosstalk between different data subsets during the storage process, thereby improving the storage independence and security of the data subsets. Specifically, based on the data read and write operation process of all container units under the data storage array, it is determined whether the container unit is performing a data read and write operation. If so, it is determined that the container unit is in a busy state of data read and write; if not, it is determined that the container unit is in an idle state of data read and write, and the container unit in the idle state of data read and write is determined as a container unit in an available state, so as to limit the container unit for the subsequent storage of data subsets in the data storage array. Then, based on the data type compatibility characteristics and storage capacity of all container units in an available state, each data subset is stored in a container unit with sufficient storage capacity and compatible with the data subset type.

[0027] S200, connecting the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility status of the matching task processing result, returning the matching task processing result to the corresponding container unit for storage.

[0028] Furthermore, the application set of the cloud platform is connected to the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform, determine the data processing permission information of all applications, and integrate the applications that meet the preset data processing permission requirements into an application set; connect the application set with the data storage array based on the gateway status of the network where the cloud platform is located; and perform matching task processing on the data subset based on the running threads of all applications under the application set; Obtain thread running error occurrence parameters for matching task processing, and determine whether matching task processing results are credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

[0029] The cloud platform has higher computing power and memory resource support. Compared with the user end, it can run multiple applications stably at the same time and realize the parallel operation of multiple applications. In order to uniformly manage all applications in the cloud platform, all applications installed in the cloud platform are first verified to determine the data processing permission information of all applications, where the data processing permission information can be but not limited to the computing power resources and memory resources that the application is allowed to call during the data processing process. Applications that are allowed to call computing power resources and memory resources that do not exceed the preset number threshold (i.e., applications that meet the preset data processing permission requirements) are integrated into an application set to achieve subsequent unified resource retrieval management of all applications under the set. Then, based on the available bandwidth of all gateways under the network where the cloud platform is located (i.e., gateway status), the applications in the application set are connected to the container unit in the data storage array through the corresponding gateway, and based on the running threads of all applications under the application set, matching task processing is performed on the data subset in the container unit to ensure the accurate processing of the data subset by the application.

[0030] In addition, thread running errors are inevitable in the process of application processing tasks, which leads to errors in the running results of the corresponding threads, and continue to accumulate during the thread running process, thus affecting the credibility of the final task processing results. The modeling operation of the modeling engine is implemented based on the task processing results of all applications in the cloud platform. In order to ensure the correct modeling of the modeling engine, it is necessary to determine whether the matching task processing results of the application are credible, and return the credible matching task processing results to the container unit where the corresponding data subset is stored, so that the modeling engine can accurately retrieve the required matching task processing results at any time for modeling operations. At the same time, the container unit where the data subset is located is cleaned, that is, the original data subset stored in the container unit is cleared to avoid crosstalk between the data subset in the container unit and the matching task processing results.

[0031] S300, based on the modeling requirement characteristics of the modeling engine, selecting a matching container unit from the data storage array and loading it into the modeling engine; based on the modeling state characteristics of the modeling engine, performing change processing on the matching container unit.

[0032] Further, based on the modeling requirement characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed, specifically: The knowledge graph of the modeling engine is parsed to determine the metadata type features required by the modeling engine in the current modeling process, and this is used as the modeling requirement features; the metadata type features are compared with the container units storing the matching processing results in the data storage array to determine the container units that match the current modeling process, and the mirror image of the matching container unit is loaded into the modeling engine; The modeling process of the modeling engine is monitored to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, the storage content of the used matching container units is cleared.

[0033] The modeling engine can be, but is not limited to, an engine that uses the computing power and memory resources of the cloud platform to perform modeling operations. In this way, the modeling engine does not need to have its own resource library, thus achieving lightweight modeling engines. The modeling operation of the modeling engine is implemented based on the corresponding knowledge graph, and the knowledge graph represents the process of the modeling operation. In order to provide a reliable modeling data source for the entire modeling operation of the modeling engine, it is necessary to first determine the modeling demand characteristics of the modeling engine (that is, the entire modeling operation of the modeling engine includes the data source required for each modeling process), and compare it with the matching processing results stored in all container units in the data storage array to determine the required data source corresponding to each modeling process of the modeling engine, so as to accurately load the image of the container unit into the modeling engine, so that the modeling engine can directly and accurately retrieve data from the container unit for modeling purposes. In addition, when the modeling engine completes a certain modeling process and does not need to continue to retrieve the data in the container unit, the storage content of the container unit can be cleared at this time to avoid the container unit being occupied for a long time and improve the recycling efficiency of the container unit.

[0034] S400, based on the running status characteristics of the user end, returning the modeling result of the modeling engine to the operation decision task thread of the user end; based on the execution status characteristics of the operation decision task thread, sending a control instruction to the device end under the user end.

[0035] Furthermore, based on the running status characteristics of the user end, the modeling results of the modeling engine are returned to the operation decision task thread of the user end; based on the execution status characteristics of the operation decision task thread, a control instruction is sent to the device end under the user end, specifically: Parse and process the operation decision-making operation instruction received by the user end to obtain the state characteristics of the operation decision-making operation environment of the user end, so as to judge whether the user end meets the preset operation decision-making operation environment conditions; if the preset operation decision-making operation environment conditions are met, return the modeling result of the modeling engine to the operation decision task thread of the user end; Based on the execution progress status characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted; based on the complete operation decision result, a work behavior control instruction is sent to the device end under the user end.

[0036] The user end is directly controlled and connected to the equipment end for executing the production process, so that the user end can adjust the operation state of the equipment end separately and coordinate the operation relationship between different equipment ends. In order to improve the control and adjustment performance of the user end to the equipment end, the modeling results generated by the modeling engine are used to guide the control strategy of the user end to the equipment end. For this purpose, the operation decision operation instruction received by the user end is parsed and processed to obtain the state characteristics of the operation decision operation environment of the user end (such as the performance parameters of the operation decision operation hardware environment and software environment of the user end), so as to judge whether the user end meets the preset operation decision operation environment conditions, that is, whether it meets the performance requirements of the preset operation decision operation hardware environment and software environment. When the performance requirements of the preset operation decision operation hardware environment and software environment are met, the modeling results of the modeling engine are returned to the operation decision task thread of the user end, so that the user end can use the modeling results as the basis for operation decision. In addition, based on the execution progress state characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted, so as to instruct the user end to send work behavior control instructions to the subordinate equipment end, so that the equipment end can adjust its own working state, so as to match the environmental conditions of the park and / or coordinate with other equipment ends in the work process.

[0037] See also Figure 2 As shown, the present invention provides a full-dimensional digital operation decision-making system, which includes the following modules: The data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; A data storage control module, used for storing all data subsets in corresponding container units under the data storage array based on the storage state characteristics of the data storage array; The data processing control module is used to connect the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit; A modeling engine control module, used for selecting matching container units from the data storage array and loading them into the modeling engine based on the modeling requirement characteristics of the modeling engine; A container change processing module is used to perform change processing on the matching container unit based on the modeling state characteristics of the modeling engine; The operation decision execution module is used to return the modeling result of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal; The instruction sending module is used to send control instructions to the device end under the user end based on the execution status characteristics of the operation decision task thread.

[0038] Furthermore, the data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, so that the full-dimensional data set is divided into several data subsets.

[0039] Furthermore, the data storage control module is used to store all data subsets in corresponding container units under the data storage array based on the storage state characteristics of the data storage array, specifically: Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units in the data storage array that are in an available state; based on the data type compatibility characteristics and storage capacity of all container units in an available state, store all data subsets in corresponding container units that are in an available state.

[0040] Furthermore, the data processing control module is used to connect the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform, determine the data processing permission information of all applications, and integrate the applications that meet the preset data processing permission requirements into an application set; connect the application set with the data storage array based on the gateway status of the network where the cloud platform is located; and perform matching task processing on the data subset based on the running threads of all applications under the application set; Obtain thread running error occurrence parameters for matching task processing, and determine whether matching task processing results are credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

[0041] Furthermore, the modeling engine control module is used to select matching container units from the data storage array and load them into the modeling engine based on the modeling requirement characteristics of the modeling engine, specifically: The knowledge graph of the modeling engine is parsed to determine the metadata type features required by the modeling engine in the current modeling process, which are used as modeling requirement features; the metadata type features are compared with the container units storing matching processing results in the data storage array to determine the container units that match the current modeling process, and the mirror image of the matching container unit is loaded into the modeling engine.

[0042] Furthermore, the container change processing module is used to perform change processing on the matching container unit based on the modeling state characteristics of the modeling engine, specifically: The modeling process of the modeling engine is monitored to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, the storage content of the used matching container units is cleared.

[0043] Furthermore, the operation decision execution module is used to return the modeling results of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal, specifically: The operation decision-making operation instructions received by the user end are parsed and processed to obtain the operation decision-making operation environment status characteristics of the user end, so as to determine whether the user end meets the preset operation decision-making operation environment conditions; if the preset operation decision-making operation environment conditions are met, the modeling result of the modeling engine is returned to the operation decision task thread of the user end.

[0044] Furthermore, the instruction issuing module is used to send control instructions to the device end under the user end based on the execution state characteristics of the operation decision task thread, specifically: Based on the execution progress status characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted; based on the complete operation decision result, a work behavior control instruction is sent to the device end under the user end.

[0045] The operation and effects of the full-dimensional digital operation decision system of the present invention are corresponding to and consistent with the above-mentioned full-dimensional digital operation decision method, and the full-dimensional digital operation decision system will not be repeated here.

[0046] In one embodiment of the present invention, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0047] In one embodiment of the present invention, the present invention further provides a computer device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described above is implemented.

[0048] Through the description of the above implementation methods, technicians in this field can clearly understand that each implementation method can be implemented by adding a necessary general hardware platform, and of course can also be implemented by combining hardware and software. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a computer product, and the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it, and other embodiments may also be used. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-dimensional digital operation decision-making method, characterized in that: The method comprises the following steps: Monitor all user terminals in the park, collect full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; based on the storage state characteristics of the data storage array, store all data subsets in the corresponding container units under the data storage array; Connecting the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; returning the matching task processing result to the corresponding container unit based on the credibility of the matching task processing result; Based on the modeling requirement characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed; Based on the running status characteristics of the user terminal, the modeling result of the modeling engine is returned to the operation decision task thread of the user terminal; based on the execution status characteristics of the operation decision task thread, a control instruction is sent to the device terminal under the user terminal.

2. The method according to claim 1, characterized in that The monitoring of all user terminals in the park, collecting the full-dimensional data sets of all user terminals, dividing the full-dimensional data sets into a number of data subsets based on the data attributes of the full-dimensional data sets; based on the storage state characteristics of the data storage array, storing all data subsets in the corresponding container units under the data storage array, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, so as to divide the full-dimensional data set into a number of data subsets; Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units under the data storage array that are in an available state; Based on the data type compatibility characteristics and storage capacity of all container units in an available state, all data subsets are stored in corresponding container units in an available state.

3. The method according to claim 1, characterized in that The application set of the cloud platform is connected to the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform and determine the data processing permission information of all applications, so as to integrate the applications that meet the preset data processing permission requirements into an application set; Based on the gateway status of the network where the cloud platform is located, the application set is connected to the data storage array; and based on the running threads of all applications under the application set, matching task processing is performed on the data subset; Obtaining thread running error occurrence parameters of the matching task processing, and judging whether the matching task processing result is credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

4. The method according to claim 1, characterized in that: Based on the modeling requirement characteristics of the modeling engine, a matching container unit is selected from the data storage array and loaded into the modeling engine; based on the modeling state characteristics of the modeling engine, the matching container unit is changed, specifically: Parsing the knowledge graph of the modeling engine to determine the metadata type features required by the modeling engine in the current modeling process as the modeling requirement features; Compare the metadata type feature with the container unit storing the matching processing result in the data storage array to determine the container unit matching the current modeling process, and load the image of the matching container unit into the modeling engine; The modeling process of the modeling engine is monitored to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, the storage content of the used matching container units is cleared.

5. The method according to claim 1, characterized in that Based on the running state characteristics of the user terminal, the modeling result of the modeling engine is returned to the operation decision task thread of the user terminal; based on the execution state characteristics of the operation decision task thread, a control instruction is sent to the device terminal under the user terminal, specifically: Parsing and processing the operation decision operation instruction received by the user terminal to obtain the operation decision operation environment state characteristics of the user terminal, thereby judging whether the user terminal meets the preset operation decision operation environment conditions; if the preset operation decision operation environment conditions are met, returning the modeling result of the modeling engine to the operation decision task thread of the user terminal; Extracting the complete operation decision result output by the operation decision task thread based on the execution progress status characteristics of the operation decision task thread; Based on the complete operation decision result, a work behavior control instruction is sent to the device terminal under the user terminal.

6. A full-dimensional digital operation decision-making system, characterized in that: The system includes the following modules: A data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets; A data storage control module, used for storing all data subsets into corresponding container units under the data storage array based on the storage state characteristics of the data storage array; A data processing control module, used to connect the application set of the cloud platform with the data storage array, so as to perform matching task processing on the data subset; Based on the credibility status of the matching task processing result, returning the matching task processing result to the corresponding container unit for storage; A modeling engine control module, used for selecting a matching container unit from the data storage array and loading it into the modeling engine based on the modeling requirement characteristics of the modeling engine; A container change processing module, used for performing change processing on the matched container unit based on the modeling state characteristics of the modeling engine; An operation decision execution module, used for returning the modeling result of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal; The instruction sending module is used to send control instructions to the device terminal under the user terminal based on the execution state characteristics of the operation decision task thread.

7. The system according to claim 6, characterized in that The data collection and division module is used to monitor all user terminals in the park, collect the full-dimensional data sets of all user terminals, and divide the full-dimensional data sets into several data subsets based on the data attributes of the full-dimensional data sets, specifically: Based on the work logs of all user terminals in the park, determine the software tools in normal state in each user terminal; based on the interface bandwidth of the software tools in normal state at the user terminal, determine the monitoring operation parameters for the software tools in normal state, so as to collect a full-dimensional data set from all software tools in normal state in each user terminal; Based on the type attributes and timeliness attributes of all data under the full-dimensional data set, all data are marked, so as to divide the full-dimensional data set into a number of data subsets; The data storage control module is used to store all data subsets in corresponding container units under the data storage array based on the storage state characteristics of the data storage array, specifically: Based on the data read and write status of all container units under the data storage array, determine the data read and write busy and idle attributes of all container units; based on the data read and write busy and idle attributes, identify all container units under the data storage array that are in an available state; Based on the data type compatibility characteristics and storage capacity of all container units in an available state, all data subsets are stored in corresponding container units in an available state.

8. The system according to claim 6, characterized in that The data processing control module is used to connect the application set of the cloud platform with the data storage array to perform matching task processing on the data subset; based on the credibility of the matching task processing result, the matching task processing result is returned and stored in the corresponding container unit, specifically: Verify all applications installed in the cloud platform and determine the data processing permission information of all applications, so as to integrate the applications that meet the preset data processing permission requirements into an application set; Based on the gateway status of the network where the cloud platform is located, the application set is connected to the data storage array; and based on the running threads of all applications under the application set, matching task processing is performed on the data subset; Obtaining thread running error occurrence parameters of the matching task processing, and judging whether the matching task processing result is credible based on the thread running error occurrence parameters; After cleaning the container unit where the data subset is located, all credible matching task processing results are returned and stored in the corresponding container unit.

9. The system according to claim 6, characterized in that The modeling engine control module is used to select a matching container unit from the data storage array and load it into the modeling engine based on the modeling requirement characteristics of the modeling engine, specifically: Parsing the knowledge graph of the modeling engine to determine the metadata type features required by the modeling engine in the current modeling process as the modeling requirement features; Compare the metadata type feature with the container unit storing the matching processing result in the data storage array to determine the container unit matching the current modeling process, and load the image of the matching container unit into the modeling engine; The container change processing module is used to perform change processing on the matched container unit based on the modeling state characteristics of the modeling engine, specifically: Monitor the modeling process of the modeling engine to obtain the modeling progress status characteristics of the modeling engine; based on the modeling progress status characteristics, clear the storage content of the used matching container units; The operation decision execution module is used to return the modeling result of the modeling engine to the operation decision task thread of the user terminal based on the operation status characteristics of the user terminal, specifically: Parsing and processing the operation decision operation instruction received by the user terminal to obtain the operation decision operation environment state characteristics of the user terminal, thereby judging whether the user terminal meets the preset operation decision operation environment conditions; if the preset operation decision operation environment conditions are met, returning the modeling result of the modeling engine to the operation decision task thread of the user terminal; The instruction issuing module is used to send a control instruction to the device terminal under the user terminal based on the execution state characteristics of the operation decision task thread, specifically: Based on the execution progress status characteristics of the operation decision task thread, the complete operation decision result output by the operation decision task thread is extracted; based on the complete operation decision result, a work behavior control instruction is sent to the device terminal under the user terminal.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 5.

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