Full life cycle quality credible data acquisition method and related equipment

Abstract

One or more embodiments of the invention provide a full life cycle quality trusted data acquisition method and related equipment. The method is applied to a distributed control system and comprises the steps that updated data density is determined based on current full-life-cycle quality data, and the data density is used for representing the relative closeness degree between credible data; determining a trusted data deployment node based on the updated data density; and constructing a trusted data path model based on the structure of the trusted data deployment node, and screening target trusted data in the trusted data path model. Through the technical scheme of the invention, the data acquisition stability can be effectively improved, the data acquisition delay is reduced, and the influence of an error elimination period on data transmission is reduced.

Description

Technical Field

[0001] One or more embodiments of the present application relate to the technical field, and in particular, to a method for collecting trusted quality data throughout the life cycle and related equipment. Background Art

[0002] Distributed Control System (DCS) is a new control system based on microprocessors, featuring decentralized control of dangers and centralized operation and management, integrating advanced computer technology, communication technology, CRT technology and control technology, namely 4C technology. With the rapid development of modern computer and communication network technology, DCS is developing in the direction of diversification, networking, openness and integrated management, so that different types of DCS can be interconnected and exchange data, and the DCS system can be connected to the factory management network through Ethernet to realize real-time data access to the Internet, becoming the mainstream of process industry automatic control.

[0003] Generally speaking, the service life of DCS equipment is 20 years, which can be extended through upgrades and repairs. Therefore, it is necessary to obtain the full life cycle data of DCS equipment to formulate a maintenance plan for DCS equipment.

[0004] In the related art, the display value data of the DCS equipment is collected, and the reliable data in the above display value data is screened by using methods such as the optimization objective function. However, this method requires the collection of all the quality data of the entire life cycle before screening, and ignores the attribute characteristics of the dynamic change of data density. The related art has the problems of low data collection efficiency, large influence of the error elimination cycle on data transmission, small amount of reliable data collection and data collection delay. Summary of the invention

[0005] In view of this, an object of one or more embodiments of the present application is to propose a method for collecting trusted quality data throughout the entire life cycle and related equipment to solve the problems pointed out in the background technology.

[0006] Based on the above objectives, one or more embodiments of the present application provide a method for collecting reliable quality data throughout the life cycle, which is applied to a distributed control system, including:

[0007] Determine updated data density based on current life cycle quality data, wherein the data density is used to indicate the relative closeness between trusted data;

[0008] Determining a trusted data deployment node based on the updated data density;

[0009] A trusted data path model is constructed based on the structure of the trusted data deployment node, and target trusted data in the trusted data path model is screened.

[0010] Optionally, the step of calculating the updated data density includes:

[0011] Based on the current full life cycle quality data, the possibility of any full life cycle quality data becoming credible data is obtained;

[0012] Based on the likelihood, an updated data density is obtained.

[0013] Optionally, obtaining the possibility of any life cycle quality data becoming credible data based on the current life cycle quality data includes:

[0014] Based on the current full life cycle quality data, the possibility of any full life cycle quality data becoming credible data is obtained through the following formula;

[0015]

[0016] Wherein, i represents any of the life cycle quality data, j represents another life cycle quality data, α represents the influence of pheromone concentration on path selection during ant crawling, β represents the importance of selecting life cycle quality data under ideal conditions, represents the data density at time t, represents the length of the ant's crawling from the full life cycle quality data i to the full life cycle quality data j in an ideal situation, κ represents the actual crawling position of the ant colony, G represents the allowed crawling range of the ant colony, It represents the pheromone obtained by the ant colony of the sth sample at time t when crawling.

[0017] Optionally, obtaining the updated data density based on the possibility includes: obtaining the updated data density based on the possibility by using the following formula;

[0018] ρ i ' j (t) = p ij (t)+Δρ ij ;

[0019] Among them, Δρ ij Represents the pheromone concentration generated by the ant colony when the expected data is selected.

[0020] Optionally, the trusted data deployment node includes a fixed trusted data deployment node and a dynamic trusted data deployment node;

[0021] The step of determining the trusted data deployment node includes:

[0022] Determining the collection similarity of the trusted data based on a preset fixed trusted data deployment node and the updated data density;

[0023] Based on the collection similarity, the dynamic trusted data deployment node is determined.

[0024] Optionally, the determining the dynamic trusted data deployment node based on the collection similarity includes:

[0025] Based on the collection similarity, determining the spacing of the trusted data deployment nodes;

[0026] Based on the distance, the dynamic trusted data deployment node is determined.

[0027] Optionally, the spacing is obtained by the following formula:

[0028]

[0029] in, Represents the total coverage of trusted data collection, It represents the unit distance of the trusted data deployment node, e represents the number of data collection times of the trusted data node, and c represents the deviation range of trusted data collection.

[0030] Optionally, the determining the dynamic trusted data deployment node based on the collection similarity includes:

[0031] Determining attribute classification of credible data based on the collection similarity;

[0032] Based on the collection similarity and target attribute classification, the spacing of the trusted data deployment nodes is determined.

[0033] Optionally, the acquisition similarity is obtained by the following formula:

[0034]

[0035] Among them, η represents the data length, ε represents the interval frequency of data collection, V represents the foot feeling deviation, and ψ represents the collection ratio of reliable data.

[0036] Optionally, the trusted data path model includes a plurality of trusted data deployment nodes, each of which corresponds to at least one cluster head; and screening the target trusted data in the trusted data path model includes:

[0037] Determining a data transmission rate between the trusted data deployment node and a corresponding cluster head;

[0038] In response to determining that the data transmission rate is less than or equal to a preset threshold, determining the transmission data energy consumption of the trusted data deployment node using a free space model; or,

[0039] In response to determining that the data transmission rate is greater than a preset threshold, determining the transmission data energy consumption of the trusted data deployment node using a multipath fading model;

[0040] Obtaining the total energy consumption of the data collection process based on the energy consumption of transmitting data of the trusted data deployment node;

[0041] The data of the cluster head corresponding to the path with the lowest total energy consumption in the trusted data path model is determined as the updated trusted data.

[0042] Based on the same inventive concept, one or more embodiments of the present application further provide a full life cycle quality trusted data collection device, including:

[0043] A first calculation module is configured to determine an updated data density based on current full life cycle quality data, wherein the data density is used to represent a relative closeness between trusted data;

[0044] A node deployment module, configured to determine a trusted data deployment node based on the updated data density;

[0045] The second computing module is configured to build a trusted data path model based on the structure of the trusted data deployment node and screen the target trusted data in the trusted data path model.

[0046] Optionally, the first calculation module is specifically used to: obtain the possibility of any full life cycle quality data becoming credible data based on the current full life cycle quality data; and obtain updated data density based on the possibility.

[0047] Optionally, the node deployment module is specifically used to: determine the collection similarity of the trusted data based on preset fixed trusted data deployment nodes and the updated data density; and determine the dynamic trusted data deployment node based on the collection similarity.

[0048] Optionally, the second calculation module is specifically used to: determine the data transmission rate between the trusted data deployment node and the corresponding cluster head; in response to determining that the data transmission rate is less than or equal to a preset threshold, determine the transmission data energy consumption of the trusted data deployment node using a free space model; or, in response to determining that the data transmission rate is greater than a preset threshold, determine the transmission data energy consumption of the trusted data deployment node using a multipath fading model; obtain the total energy consumption of the data acquisition process based on the transmission data energy consumption of the trusted data deployment node; and determine that the data of the cluster head corresponding to the path with the lowest total energy consumption in the trusted data path model is the updated trusted data.

[0049] Based on the same inventive concept, one or more embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for collecting trusted quality data throughout the life cycle as described in any one of the above is implemented.

[0050] Based on the same inventive concept, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the above-mentioned methods for collecting trusted quality data throughout the life cycle.

[0051] From the above, it can be seen that the method for collecting trusted data on quality throughout the life cycle provided by one or more embodiments of the present application determines the updated data density based on the current full life cycle quality data; the data density is used to indicate the relative closeness between trusted data; based on the updated data density, the trusted data deployment node is determined; based on the structure of the trusted data deployment node, a trusted data path model is constructed, and the target trusted data in the trusted data path model is screened.

[0052] The method for collecting trusted data for quality throughout the life cycle provided by one or more embodiments of the present application determines the trusted data deployment node with a higher proportion of trusted data in the DCS device based on the updated data density, and further screens the trusted data in the trusted data path model constructed by the trusted data deployment node. The technical solution of the present application can effectively improve the stability of data collection, reduce data collection delay, and reduce the impact of the error elimination cycle on data transmission.

[0053] The device, electronic device and computer-readable storage medium for collecting trusted data on quality throughout the life cycle provided in the present application are all capable of implementing the steps of the above-mentioned method for collecting trusted data on quality throughout the life cycle, and therefore also have the beneficial effects of the above-mentioned method for collecting trusted data on quality throughout the life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate one or more embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flowchart of a method for collecting reliable quality data throughout the life cycle of one or more embodiments of the present application;

[0056] Figure 2 A schematic diagram of the structure of a reliable data collection device for quality throughout the life cycle of one or more embodiments of the present application;

[0057] Figure 3 This is a graph showing experimental results of the influence of the error elimination period on data transmission according to one or more embodiments of the present application;

[0058] Figure 4 This is a graph of data acquisition stability test results for one or more embodiments of the present application;

[0059] Figure 5 A schematic diagram of the hardware structure of an electronic device according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0061] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0062] As mentioned in the background technology section, DCS equipment is the main means for power plants and industrial enterprises such as oil to collect important internal data. DCS can promptly feedback the data required by the enterprise, and has the functions of decentralized and centralized control, which can greatly improve the industry's production capacity and management level, and enhance the flexibility and adaptability of the enterprise. Normally, the service life of DCS equipment is 20 years, but the service life of DCS equipment can be extended through upgrades and repairs. Specifically, it is necessary to obtain accurate data on the entire life cycle of DCS equipment. Based on the above accurate data and the current life cycle of DCS equipment, formulating a personalized maintenance plan can effectively extend the life cycle of DCS equipment.

[0063] The above scheme can be regarded as two stages: data collection and scheme customization. For the data collection stage, the solutions of related technologies include two categories. The first type of solution first collects all the quality data of the entire life cycle of the DCS equipment in an iterative round-robin manner, and then filters out the reliable data through the preset data optimization objective function; the second type of solution also collects all the quality data of the entire life cycle of the DCS equipment in an iterative round-robin manner, and then uses the preset data feature statements to retrieve the data to obtain the reliable data. There are other solutions in the related technology to achieve data collection.

[0064] However, the above solutions are all based on the data processing of all the quality data of the whole life cycle on the DCS equipment to obtain reliable data. There is a lot of useless information in the collection results of the quality data of the whole life cycle, which leads to the need to consume a lot of computing power and time resources for collection and data processing, low work efficiency, poor effectiveness of collection results, and the existence of problems such as the large degree of influence of the error elimination cycle on data transmission, small amount of reliable data collection, and data collection delay.

[0065] Therefore, the full life cycle quality trusted data collection method of one or more embodiments of the present application can effectively improve the stability of data collection, reduce data collection delay, and reduce the impact of the error elimination cycle on data transmission through the technical solution of the present application.

[0066] refer to Figure 1 The method for collecting reliable quality data throughout the life cycle of one or more embodiments of the present application includes the following steps:

[0067] Step S101: Based on the current full life cycle quality data, determine the updated data density, wherein the above data density is used to represent the relative closeness between the trusted data.

[0068] The above-mentioned full life cycle quality data represents data that can characterize the life cycle of DCS equipment. The above-mentioned full life cycle quality data may include one or more of the display data, operation data, fault data and maintenance data of the DCS equipment. The above-mentioned full life cycle quality data may also be other DCS data. This application does not limit the type and quantity of the full life cycle quality data.

[0069] Optionally, the above-mentioned life cycle quality data can be obtained by collecting equipment display data, or from workers' maintenance records, or from monitoring equipment of DCS equipment. This application does not limit the method of obtaining the life cycle quality data.

[0070] The updated data density is determined based on the current full life cycle quality data, which means that the updated data density is obtained according to the distribution of credible data in the full life cycle quality data.

[0071] Optionally, the updated data density can be obtained based on an ant colony algorithm. The ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants and is used to solve the graph path optimization problem. The ant colony algorithm is based on the fact that ants secrete pheromones to mark paths and guide the behavior of other ants when looking for food sources and returning to their nests. This pheromone will evaporate over time, and the pheromone on the path used by more ants will be more concentrated, causing more ants to choose this path.

[0072] In this application, it is considered that when several ants access a path from one data to another data at the same time, the data attributes under the path need to be updated to obtain the pheromone concentration. The pheromone concentration at this time represents the quantitative relationship between different factors in terms of mutual connection, interaction, and change in system state. The above path can pass through multiple data. There can be multiple paths in a database or data set.

[0073] Based on the above content, in some embodiments of the present application, the step of determining the updated data density includes: based on the current full life cycle quality data, obtaining the possibility of any full life cycle quality data becoming credible data through the following formula; Wherein, i represents any of the life cycle quality data, j represents another life cycle quality data, α represents the influence of pheromone concentration on path selection during ant crawling, β represents the importance of selecting life cycle quality data under ideal conditions, represents the data density at time t, represents the length of the ant's crawling from the full life cycle quality data i to the full life cycle quality data j in an ideal situation, κ represents the actual crawling position of the ant colony, G represents the allowed crawling range of the ant colony, represents the pheromone obtained by the ant colony of the sth sample at time t when crawling; based on the possibility, the updated data density is obtained by the following formula; ρ i ' j (t) = p ij (t)+Δρ ij ; where Δρ ij Represents the pheromone concentration generated by the ant colony when the expected data is selected.

[0074] It should be noted that the importance of data selection cannot be ignored, and it directly affects the performance and results of the algorithm. Ideally, data selection should be able to fully reflect the characteristics and requirements of the problem, so that ants can efficiently discover and use useful information during the search process. The importance of the above-mentioned full-life cycle quality data selection is automatically completed by the ant colony algorithm according to certain rules and mechanisms. In the ant colony algorithm, each ant will select the next data point to visit based on the current pheromone distribution and heuristic information (such as distance, quality, etc.). As the algorithm iterates, the ants will continuously update the pheromone, thereby gradually discovering better paths or data combinations.

[0075] The timing for calculating the updated data density is when the ants complete the path selection and reach the terminal. At this time, it is considered that the ant colony algorithm has completed a cycle and the data density can be updated.

[0076] The significance of obtaining the updated data density through this step is to effectively utilize the pheromone in the ant colony algorithm, reduce the search space, improve the convergence speed of the algorithm of this application, and reduce the data processing burden.

[0077] Specifically, when the data density is high, it means that the data points are concentrated in a certain area. Combined with the ant colony algorithm, high-density data points will produce more pheromone accumulation. Such high-density data distribution is conducive to ants finding high-concentration pheromones in the area more quickly and making decisions based on these pheromones, reducing search time. When the data presents a high density, the potential path or solution space that the algorithm needs to search will be relatively reduced. When ants are looking for the optimal path, they can traverse these points faster and reduce unnecessary search steps. In addition, as the iteration proceeds, pheromones will gradually accumulate on the better path, forming a positive feedback mechanism. When the data density is high, this positive feedback effect will be more obvious because ants can perceive high-concentration pheromones in a shorter time. This helps the algorithm converge to the optimal solution faster and reduce the total aggregation time. High data density also means that the algorithm can use memory and computing resources more efficiently when processing data, because the correlation and similarity between data points are high, and the algorithm may need relatively less computing when aggregating data.

[0078] Since the life cycle of DCS equipment changes in real time, data density updates can timely obtain the latest reliable data on the quality of the entire life cycle of DCS equipment, ensuring that the collected data information is current and accurate. This allows for more accurate calculation of node deployment spacing and reduces misjudgments or wrong decisions caused by outdated or inaccurate data.

[0079] Step S102: Determine a trusted data deployment node based on the updated data density.

[0080] Based on the updated data density obtained in step S101, it is necessary to further determine the trusted data deployment node for collecting trusted data.

[0081] In the technical solution of the present application, in order to ensure the efficiency of automated collection of trusted data, it is necessary to further extract and classify the data to obtain specific data trust features in order to obtain a suitable node deployment solution.

[0082] Optionally, the present application can use collection similarity to represent the characteristics between trusted data, and determine the above-mentioned trusted data deployment node based on the above-mentioned collection similarity. Among them, collection similarity refers to comparing the similarity or consistency between data collected at different time points or different detection nodes during the continuous data collection process. This similarity calculation helps to identify outliers, noise or potential deviations in the data, thereby ensuring the reliability and accuracy of the collected data.

[0083] Specifically, the calculation formula of the above acquisition similarity can be expressed as: Among them, η represents the data length, ε represents the interval frequency of data collection, V represents the foot feel deviation, and ψ represents the collection ratio of reliable data. Among them, the collection ratio refers to the proportional relationship between the collection frequency, data volume or data type between different detection nodes or different data sources during the data collection process. The collection ratio may involve how to balance the data contribution of different data sources to ensure the accuracy and completeness of the overall data. For example, in some cases, some detection nodes may be more critical than other nodes, and therefore require a higher collection frequency or a larger amount of data. The setting of the collection ratio should be based on actual needs and the importance of the data to ensure the comprehensive quality of the data. Foot feel deviation is used to describe deviations related to data collection or data processing.

[0084] In some practical application scenarios, the time required for a single data collection is 0.5 minutes, and the data conversion ratio is 1.2. Among them, one-way data refers to the collection space formed by the fixed detection nodes set in the DCS equipment, and one-way data refers to a data set that is independently and completely transmitted from each node to the data processing center. These data sets together constitute the trusted data foundation of the entire life cycle of the equipment, providing key support for subsequent data analysis and equipment status monitoring. The data conversion ratio refers to a certain conversion relationship between the original data and the actual required data during the data collection process. This conversion may involve data magnification, reduction, unit conversion or other forms of mathematical transformation. For example, if the sensor measures data expressed in a certain unit, and the actual analysis requires data in another unit or ratio, then it needs to be adjusted through the conversion ratio. Specifically, the 1.2 conversion ratio here means that the data collected each time needs to be multiplied by 1.2 to obtain the actual analysis value. The above formula is obtained under the above application scenario.

[0085] Preferably, attribute features of the trusted data may also be acquired based on the above-mentioned acquisition similarity, and a directional data packet may be constructed based on the above-mentioned attribute features to meet specific application scenarios or business requirements.

[0086] Specifically, similarity calculations can help discover patterns, trends, and outliers in data. When two or more data points show high similarity in similarity calculations, they may share certain attribute features. These features may include the distribution, range, and change trend of the data. By analyzing the common features between these similar data points, some attribute features of the entire data set can be inferred. In some embodiments, the step of determining the attribute features obtained by the similarity calculation includes: selecting a suitable similarity calculation method, such as Euclidean distance, cosine similarity, Pearson correlation coefficient, etc. according to the characteristics and requirements of the data; calculating a similarity matrix, using the selected similarity calculation method, calculating the similarity between all data points in the data set to obtain a similarity matrix; analyzing the similarity matrix, identifying clusters of data points with high similarity by observing and analyzing the similarity matrix. These clusters may correspond to certain common attribute features; extracting attribute features, extracting attribute features of the data based on the analysis results of the similarity matrix. These features can be numerical (such as mean, standard deviation, etc.) or descriptive (such as data distribution pattern, change trend, etc.); verification and evaluation, by comparing and verifying with actual data or domain knowledge, evaluate the accuracy and effectiveness of the extracted attribute features.

[0087] In some embodiments, after obtaining the above-mentioned attribute characteristics, a unique conversion format can also be constructed to complete the classification of data attributes, so that subsequent data collection can constitute a directional data packet. The conversion format refers to converting the original collected data or the data after preliminary processing into a standardized, unified format suitable for subsequent analysis. This conversion is intended to ensure the consistency and comparability of the data, so as to more effectively extract the attribute characteristics of the data, complete the classification of the data attributes, and finally construct a directional data packet. A directional data packet refers to a data packet constructed according to specific business needs or data analysis goals. It contains specific data that has been converted and classified to meet specific application scenarios or business needs. The construction of a directional data packet is based on the classification and conversion format of data attributes to ensure that the data in the data packet is closely related to the business needs, thereby improving the efficiency of data collection, transmission and processing. This conversion format may include the following steps: data analysis and preprocessing, first, an in-depth analysis of the original data is performed to understand its data structure, data type, data range, etc. Through data cleaning and preprocessing, outliers, missing values, etc. are removed to ensure the accuracy and completeness of the data; determine the conversion target, and determine the target format of the conversion according to business needs and data characteristics. This may include data representation (such as numeric, text, etc.), storage structure (such as relational database, NoSQL database, etc.), and data granularity (such as seconds, minutes, etc.); Select or develop conversion tools: According to the conversion goals, select appropriate conversion tools or algorithms, or develop customized conversion programs according to specific needs. Implement conversion and verification, convert the original data or preliminarily processed data through conversion tools or algorithms, and verify and test the converted data to ensure that it meets business needs and data quality requirements.

[0088] After obtaining the above-mentioned collection similarity, the trusted data deployment node can be deployed. By deploying trusted deployment data nodes, the collected data links can be closely connected together to form a circular and synchronous data collection structure to improve the management capabilities of the equipment. Among them, the circular data collection structure refers to the formation of a closed loop in the data collection process, and the data circulates continuously in the collection, processing, analysis, feedback and other links. This structure can ensure the real-time and continuity of the data, and help to discover and deal with problems in a timely manner. The synchronous data collection structure refers to multiple data collection points collecting data at the same time to ensure that the data collected at each point is consistent in time. This structure can eliminate the problem of data inconsistency caused by time differences and improve the accuracy and comparability of data.

[0089] In some embodiments, the above-mentioned trusted data deployment nodes include fixed trusted data deployment nodes and dynamic trusted data deployment nodes. The fixed trusted data deployment nodes can be set by pre-deployment. The method of pre-deploying nodes for the full life cycle data of DCS equipment mainly involves pre-planning and setting data collection points in key links such as design, production, operation, and maintenance of DCS equipment.

[0090] Dynamic trusted data deployment nodes can be dynamically laid out according to DCS equipment. Specifically, the frequency and accuracy of data collection can be adjusted in real time according to changes in network traffic, differences in equipment performance, and the importance of data, ensuring that key data is collected in a timely and accurate manner. Dynamic trusted data deployment nodes can also intelligently select the type of data and information to be collected based on the operating status and fault warnings of the equipment, further improving the pertinence and availability of data. Dynamic layout can collect data in any environment, thereby achieving regional trusted data collection.

[0091] The distance between trusted data deployment nodes can be obtained by the following formula: in, Represents the total coverage of trusted data collection, It represents the unit distance of the trusted data deployment node, e represents the number of data collection times of the trusted data node, and c represents the deviation range of trusted data collection.

[0092] Through this step, the trusted data deployment node determined based on the updated data density can be obtained. The deployment location of the trusted data deployment node determines the data collection scope and coverage capability. The dynamically deployed trusted data deployment node can cover the data area that needs to be collected in real time and ensure the comprehensiveness and accuracy of the data to assist in completing the automated collection of trusted data of DCS equipment more quickly.

[0093] Step S103: construct a trusted data path model based on the structure of the trusted data deployment node, and filter the target trusted data in the trusted data path model.

[0094] In this step, a trusted data path model is first established. In the above trusted data path model, the mark S k Represents any trusted data node, C k Indicates the cluster head corresponding to the above node.

[0095] The above-mentioned screening of target trusted data in the above-mentioned trusted data path model includes: determining the data transmission rate between the above-mentioned trusted data deployment node and the corresponding cluster head; in response to determining that the above-mentioned data transmission rate is less than or equal to a preset threshold, using a free space model to determine the transmission data energy consumption of the above-mentioned trusted data deployment node; or, in response to determining that the above-mentioned data transmission rate is greater than a preset threshold, using a multipath fading model to determine the transmission data energy consumption of the above-mentioned trusted data deployment node; based on the transmission data energy consumption of the above-mentioned trusted data deployment node, the total energy consumption of the data acquisition process is obtained; and the data of the cluster head corresponding to the path with the lowest total energy consumption in the above-mentioned trusted data path model is determined as the above-mentioned updated trusted data.

[0096] Specifically, the data transmission rate between the above-mentioned trusted data deployment node and the corresponding cluster head can be calculated by the following steps: Determine the line-of-sight link probability between the above-mentioned trusted data deployment node and the corresponding cluster head Wherein, θ represents the angle between the above-mentioned trusted data deployment node and the corresponding cluster head; based on the above-mentioned line-of-sight link probability, the average path loss between the above-mentioned trusted data deployment node and the corresponding cluster head is calculated in, represents the average path loss of the above trusted data deployment node line-of-sight link, P NLoS represents the signal path loss of the non-line-of-sight link of the above trusted data deployment node, represents the average path loss of the non-line-of-sight link of the trusted data deployment node.

[0097] Optionally, the path loss and The calculation formulas are: Among them, f c represents the data carrier frequency, H represents the size of the acquisition device, ξ NLoS and LoS represent the additional path loss under non-line-of-sight and line-of-sight conditions, respectively.

[0098] Therefore, the data transmission rate expression between the above trusted data deployment node and the cluster head is: Among them, R Ck represents the rate at which data is transmitted in the node, B represents the available bandwidth, N0 represents the power spectral density of the noise in the DCS equipment, Represents the power of the trusted data deployment node.

[0099] In the technical solution of this application, the relationship between the data transmission rate and the transmission data energy consumption of the trusted data deployment node is expressed as follows: Among them, l represents the trusted data of DCS equipment, represents the energy consumption of node data transmission, E elec Indicates the power consumed by the node during transmission and reception. represents the length between the cluster head and the node, d0 represents the length threshold, ε mp represents the energy parameter of the free space model, ε fs Represents the energy parameter of the multipath fading model.

[0100] Based on the above content, the energy consumption calculation formula for receiving data by the trusted data deployment node is: If the data collection amount corresponding to all trusted data deployment nodes in the DCS equipment is the same, the energy consumed by the cluster head during data transmission is calculated as follows: Among them, |Q k | represents the number of trusted data deployment nodes, Represents the time required to collect reliable data from DCS equipment. In this way, during one cycle of data collection, the nodes in the device transmit the data to the corresponding cluster head, which then transmits it to the collection system. The total energy consumption of data collection is: K represents the number of data clusters.

[0101] As mentioned above, each trusted data deployment node corresponds to a cluster head. The above K data clusters correspond to K paths, and the starting points of the K paths correspond to the same trusted data deployment node.

[0102] Select the path with the lowest total energy consumption for data collection, and transmit the trusted data in it to the collection system according to the corresponding cluster head, so as to complete the automatic collection of trusted data. Specifically, according to the deployed DCS equipment data transmission nodes, it is concluded that there are K clusters in the data collection process, and it is necessary to collect the trusted data in the clusters according to the optimal path. Assuming that the initial node position of data collection is S0, the data collection path is Q = {S0, Q1, ..., Q K}. Thus, the automatic data collection is realized, and its expression is:

[0103]

[0104] In order to verify the technical effects of one or more embodiments of the present application, the applicant conducted a series of test experiments. The experimental parameters are set as follows: Hollysys sixth-generation DCS-TS series DCS equipment, model TC-1000, sensor type PT100 sensor. The sampling frequency of the sensor is set to 1 time per second, and the total experimental time is 60 minutes. That is, a total of 3600 data points (60 minutes * 60 seconds / minute) will be collected in the experiment.

[0105] In the experiment, a local storage device is used to store the collected temperature data. The local storage device can be an SD card. The collected temperature data is processed and analyzed using the preset data processing software. A temperature change curve can be drawn, and indicators such as average temperature, maximum value, minimum value, etc. can be calculated, and the changes in data under different settings can be analyzed. In the experiment, the collected temperature data is transmitted to the computer in real time via an Ethernet connection. Ensure the stability of the Ethernet connection and the data transmission rate to monitor the temperature data in real time.

[0106] The experiment mainly starts from three aspects, namely the impact of the error elimination cycle on data transmission, data acquisition stability test and data acquisition delay. The final experimental results of the three methods are compared under three indicators, and the data acquisition ability of the proposed method is proved in detail according to the experimental results. The pheromone heuristic factor of the ant colony algorithm is set to 0.8, the pheromone volatilization rate is 0.3, the ant colony size is 50, the data time interval is 5 minutes, the number of data is 100 samples, and the data range is between 10-100, thus completing the following experiment.

[0107] In terms of the impact of the error elimination cycle on data transmission, the premise of data collection is to ensure the integrity of the data, but some collection errors are inevitable during the collection process, and the data collection transmission volume under different error elimination cycles is also different. In order to verify the ability of the proposed method to automatically collect data, different error elimination cycles are selected, and the average data transmission volume under each error elimination cycle under different data densities is calculated. The calculation formula for the average data transmission volume during the data transmission process is: Represents the number of non-zero elements, N T represents the average number of data transmissions, υ represents the data transmission error elimination cycle, N P Represents the number of step load changes. The experimental results are as follows Figure 3 shown.

[0108] according to Figure 3 From the experimental results, it can be seen that under each error elimination cycle, the average data transmission volume corresponding to data collection under 50% data density is larger than the transmission data volume under the other two data densities, which proves that data density can be used for data screening very well and has an absolute data transmission advantage. Moreover, under different error elimination cycles, the average data transmission volume of high data density changes very little, and when the error elimination cycle reaches a certain value, the average data transmission volume of the proposed method no longer fluctuates, which proves that the error elimination cycle has little effect on the data transmission volume, and the errors can be eliminated at a larger interval period.

[0109] In terms of data collection stability testing, energy consumption is bound to occur during the trusted data collection process, and the amount of data collected will increase with time. Although increased energy consumption will enhance data collection capabilities, the lack of reasonable energy consumption planning will have a counterproductive effect. When a large amount of node energy is used in the data collection process to ensure the collection of trusted data, most nodes will die due to energy depletion after a period of time, resulting in a reduction in data collection nodes. At this time, only farther nodes or alternative nodes can be selected for data collection. In this case, not only will the social ability of the nodes be reduced, but the burden on other nodes will also be increased.

[0110] In order to explore the data collection situation of the three data credibility, the data collection volume under the three data credibility in different time periods is depicted. If the data volume increases proportionally, it proves that the energy consumption used for data collection is reasonable. If the data collection volume shows a downward trend after a period of time, it means that the initial energy consumption is high, and some nodes are exhausted later, which proves that the energy consumption used by the collection method is not reasonable. The experimental results are as follows Figure 4 shown.

[0111] By comparing the experimental data, it is found that in the data science environment, the amount of data increases steadily and orderly during the process of collecting trusted data, with no downward trend. However, in the other data environments, one method shows a sudden decrease after 1500 seconds, while the other method shows large fluctuations. This directly shows that in the two data collection environments other than the proposed method, no reasonable data trusted environment is established during the collection process, resulting in the inability to collect trusted data stably and orderly during the data collection process, making the data collection results very unstable and incomplete, which further verifies the effectiveness of the proposed method.

[0112] In terms of data collection delay, in the process of collecting trusted data, it is necessary not only to ensure the integrity and stability of data collection, but also to ensure the data collection rate. Data shows that a large number of data collection methods will have long collection delays in the later stage. In order to more intuitively observe the capabilities of the proposed method, data collection delay is regarded as a major indicator. Different databases are randomly selected and marked as database 1 to database 10, and three methods are used to collect trusted data for them respectively. The data integrity and packet loss rate collected by the three methods are set to be the same. Only the delays in three different data environments are compared. The experimental results are shown in Table 1.

[0113] Table 1

[0114]

[0115]

[0116] According to the experimental results, time delay is inevitable in the data collection process, and in each experimental environment, only the delay under the reliable data is always lower than 3.53, which proves that the data collection ability of the proposed method is better than the other two methods.

[0117] The method for collecting trusted data on quality throughout the life cycle provided in this application first updates the data density of the entire life cycle of the equipment, and secondly deploys the channels required for data collection. By judging the credibility of the data, it realizes automated collection of trusted data, thus solving the problems of the large impact of the error elimination cycle on data transmission, the small amount of data collected, and the serious delay in data collection.

[0118] It can be understood that the method can be executed by any device, equipment, platform, or device cluster having computing and processing capabilities.

[0119] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.

[0120] It should be noted that the above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a full life cycle quality trusted data collection device.

[0122] like Figure 2 As shown, the whole life cycle quality trusted data collection device includes:

[0123] A first calculation module 11 is configured to determine an updated data density based on current full life cycle quality data, wherein the data density is used to represent a relative closeness between trusted data;

[0124] The node deployment module 12 is configured to determine a trusted data deployment node based on the updated data density;

[0125] The second calculation module 13 is configured to construct a trusted data path model based on the structure of the trusted data deployment node, and screen the target trusted data in the trusted data path model.

[0126] Optionally, the first calculation module 11 is specifically used to: obtain the possibility of any full life cycle quality data becoming credible data based on the above current full life cycle quality data; and obtain updated data density based on the above possibility.

[0127] Optionally, the node deployment module 12 is specifically used to: determine the collection similarity of the trusted data based on preset fixed trusted data deployment nodes and the updated data density; and determine the dynamic trusted data deployment node based on the collection similarity.

[0128] Optionally, the above-mentioned second calculation module 13 is specifically used to: determine the data transmission rate between the trusted data deployment node and the corresponding cluster head; in response to determining that the data transmission rate is less than or equal to a preset threshold, determine the transmission data energy consumption of the trusted data deployment node using a free space model; or, in response to determining that the data transmission rate is greater than a preset threshold, determine the transmission data energy consumption of the trusted data deployment node using a multipath fading model; obtain the total energy consumption of the data acquisition process based on the transmission data energy consumption of the trusted data deployment node; and determine that the data of the cluster head corresponding to the path with the lowest total energy consumption in the trusted data path model is the updated trusted data.

[0129] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing one or more embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0130] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0131] Figure 5 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0132] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0133] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0134] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0135] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0136] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0137] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0138] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0139] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0140] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0141] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0142] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0143] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of this disclosure.

Claims

1. A method for collecting reliable quality data throughout the entire life cycle, characterized in that: Applied to distributed control systems, including: Determine updated data density based on current life cycle quality data, wherein the data density is used to indicate the relative closeness between trusted data; Determining a trusted data deployment node based on the updated data density; A trusted data path model is constructed based on the structure of the trusted data deployment node, and target trusted data in the trusted data path model is screened.

2. The method according to claim 1, characterized in that The step of calculating the updated data density comprises: Based on the current full life cycle quality data, the possibility of any full life cycle quality data becoming credible data is obtained; Based on the likelihood, an updated data density is obtained.

3. The method according to claim 2, characterized in that The possibility of obtaining any life cycle quality data as credible data based on the current life cycle quality data includes: Based on the current full life cycle quality data, the possibility of any full life cycle quality data becoming credible data is obtained through the following formula; Wherein, i represents any of the life cycle quality data, j represents another life cycle quality data, α represents the influence of pheromone concentration on path selection during ant crawling, β represents the importance of selecting life cycle quality data under ideal conditions, represents the data density at time t, represents the length of the ant's crawling from the full life cycle quality data i to the full life cycle quality data j in an ideal situation, κ represents the actual crawling position of the ant colony, G represents the allowed crawling range of the ant colony, It represents the pheromone obtained by the ant colony of the sth sample at time t when crawling.

4. The method according to claim 3, characterized in that The obtaining of updated data density based on the possibility comprises: Based on the possibility, the updated data density is obtained by the following formula: r i ′ j (t)=p ij (t)+Dr ij ; Among them, Δρ ij Represents the pheromone concentration generated by the ant colony when the expected data is selected.

5. The method according to claim 1, characterized in that The trusted data deployment nodes include fixed trusted data deployment nodes and dynamic trusted data deployment nodes; The step of determining the trusted data deployment node includes: Determining the collection similarity of the trusted data based on a preset fixed trusted data deployment node and the updated data density; Based on the collection similarity, the dynamic trusted data deployment node is determined.

6. The method according to claim 5, characterized in that The determining the dynamic trusted data deployment node based on the collection similarity includes: Based on the collection similarity, determining the spacing of the trusted data deployment nodes; Based on the distance, the dynamic trusted data deployment node is determined.

7. The method according to claim 6, characterized in that The spacing is obtained by the following formula: Wherein, D represents the acquisition similarity, Represents the total coverage of trusted data collection, It represents the unit distance of the trusted data deployment node, e represents the number of data collection times of the trusted data node, and c represents the deviation range of trusted data collection.

8. The method according to claim 5, characterized in that The determining the dynamic trusted data deployment node based on the collection similarity includes: Determining attribute classification of credible data based on the collection similarity; Based on the collection similarity and target attribute classification, the spacing of the trusted data deployment nodes is determined.

9. The method according to claim 7 or 8, characterized in that: The acquisition similarity is obtained by the following formula: Among them, η represents the data length, ε represents the interval frequency of data collection, V represents the foot feeling deviation, and ψ represents the collection ratio of reliable data.

10. The method according to claim 9, characterized in that The trusted data path model includes a plurality of trusted data deployment nodes, each of which corresponds to at least one cluster head; The screening of the target trusted data in the trusted data path model comprises: Determining a data transmission rate between the trusted data deployment node and a corresponding cluster head; In response to determining that the data transmission rate is less than or equal to a preset threshold, determining the transmission data energy consumption of the trusted data deployment node using a free space model; or, in response to determining that the data transmission rate is greater than a preset threshold, determining the transmission data energy consumption of the trusted data deployment node using a multipath fading model; Obtaining the total energy consumption of the data collection process based on the energy consumption of transmitting data of the trusted data deployment node; The data of the cluster head corresponding to the path with the lowest total energy consumption in the trusted data path model is determined as the updated trusted data.

11. A reliable data collection device for quality throughout the entire life cycle, characterized in that: include: A first calculation module is configured to determine an updated data density based on current full life cycle quality data, wherein the data density is used to represent a relative closeness between trusted data; A node deployment module, configured to determine a trusted data deployment node based on the updated data density; The second computing module is configured to build a trusted data path model based on the structure of the trusted data deployment node and screen the target trusted data in the trusted data path model.

12. The device according to claim 11, characterized in that The first calculation module is specifically used to: obtain the possibility of any full life cycle quality data becoming credible data based on the current full life cycle quality data; and obtain updated data density based on the possibility.

13. The device according to claim 12, characterized in that The node deployment module is specifically used to: determine the collection similarity of the trusted data based on the preset fixed trusted data deployment node and the updated data density; and determine the dynamic trusted data deployment node based on the collection similarity.

14. The device according to claim 13, characterized in that The second calculation module is specifically used to: determine the data transmission rate between the trusted data deployment node and the corresponding cluster head; in response to determining that the data transmission rate is less than or equal to a preset threshold, determine the transmission data energy consumption of the trusted data deployment node using a free space model; Alternatively, in response to determining that the data transmission rate is greater than a preset threshold, determining the transmission data energy consumption of the trusted data deployment node using a multipath fading model; Obtaining the total energy consumption of the data collection process based on the energy consumption of transmitting data of the trusted data deployment node; The data of the cluster head corresponding to the path with the lowest total energy consumption in the trusted data path model is determined as the updated trusted data.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 10 is implemented.

16. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute any one of claims 1 to 10.

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