Block chain-based quality infrastructure evaluation index system construction method and system

Abstract

The invention discloses a block chain-based quality infrastructure evaluation index system construction method and system, and the method comprises the steps: collecting test data of infrastructures and actual data of the infrastructures, and carrying out the preprocessing of the test data and the actual data of the infrastructures; performing relation extraction on the test data to obtain a data relation and uncertainty, and performing directed weighting according to the data relation and the uncertainty to obtain an index weight; performing state evaluation on the actual facility data to obtain an attenuation factor, and constructing a quality evaluation function according to the index weight and the attenuation factor; and constructing a quality infrastructure evaluation index system according to the quality evaluation function, and outputting a target system. The method not only can improve the precision of the block chain-based quality infrastructure evaluation index system, but also has good interpretability, and can be directly applied to a quality infrastructure evaluation index system construction system.

Description

Technical Field

[0001] The present invention relates to the field of evaluation, and in particular to a method and system for constructing a quality infrastructure evaluation index system based on blockchain. Background Art

[0002] With the rapid development of information technology, the evaluation of quality infrastructure has become a key link in ensuring the quality of products and services. However, the traditional method of constructing a quality infrastructure evaluation index system often relies on manual experience and expert judgment, which is not only inefficient but also difficult to ensure the objectivity and accuracy of the evaluation. In addition, most of the existing evaluation systems lack a dynamic adjustment mechanism and cannot reflect the actual operation status and quality changes of the infrastructure in a timely manner.

[0003] As a distributed ledger technology, blockchain technology has shown great application potential in many fields with its characteristics of decentralization, immutability and transparency. Introducing blockchain technology into the construction of the quality infrastructure evaluation index system can achieve effective management and traceability of data and improve the transparency and credibility of the evaluation. At the same time, through intelligent data processing and analysis methods, the weights of evaluation indicators can be determined more scientifically and objectively and the evaluation system can be constructed. Therefore, it is necessary to improve the scientificity, authenticity, integrity and rationality of the quality infrastructure evaluation index system. Summary of the invention

[0004] The purpose of this invention is to provide a method for constructing a quality infrastructure evaluation index system based on blockchain.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions: The present invention comprises the following steps: Collecting test data and actual facility data of the infrastructure, and preprocessing the test data and the actual facility data; Performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; Performing a status assessment on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; A quality infrastructure evaluation index system is constructed according to the quality evaluation function, and a target system is output.

[0006] Furthermore, a method for extracting a relationship from the test data to obtain a data relationship and uncertainty includes: The test data is input into the relation extraction model, and the bidirectional long short-term memory network is used to capture the correlation between the test data; Calculate the correlation: , The wth test data is , the fth test data is , test data and test data The correlation is , test data The number of relevant test data is , transposed to T, the weight parameter is , weight vector is u, test data and test data The correlation degree is , The test data with correlation greater than 0.278 are stored in pairs to obtain a correlation set; Split the test data into entities, use the test data entities as nodes, and express the nodes as follows: , Among them, the entity The corresponding node is ,entity The corresponding node is , node at layer m-1 The output of the convolutional layer is , node at layer m The weight matrix is , node at layer m The bias matrix is ; Connect the nodes according to the level and calculate the relationship coefficient: , Among them, the entity and entities The relationship embedding is ,entity and entities The relation coefficient of relation embedding is , the adjustment factor is , the activation function is , spliced ​​into ; The relationship extraction result is given according to the node representation and relationship coefficient. The expression is: , Among them, the entity and entities The relationship is The training parameters are , , the splicing entity is U, entity The weight matrix is ,entity The weight matrix is ,entity The bias matrix is ,entity The bias matrix is ; The uncertainty of random error is calculated based on the test data, and the uncertainty and relationship extraction results are output.

[0007] Furthermore, the method for obtaining the indicator weight by performing directed weighting according to the data relationship and the uncertainty includes: Construct a directed weighted network based on data relationships and uncertainties, use test data as network nodes, and connect network nodes based on data relationships; Given the loss function of a directed weighted network: , The input vector of node a is , the output vector of node a is , node a encoding layer is encoded as , the encoding layer of node x is encoded as , the edge weight between node a and node x is , the bias of node a is , the number of layers of the deep neural network encoder is , the encoder parameters of the zth layer are , the decoder parameters of the zth layer are , the Hadamard product is ; Calculate edge weights based on the association between nodes: , The edge weight between node a and node x is , the b-th data relation of node a is , the b-th data relation of node x is , the input vector of node x is , the association between node a and node x is ; Calculate the cross importance of nodes: , , , , , The i-th node is , the weight indicator function is , the node set is A, and the cross-regulation factor is ,node The output structural entropy is ,node The structural entropy of the input or output is ,node The cross-importance of ,node The number of k-th order connection points of input or output is , the maximum value function is , the minimum function is ,node The uncertainty is ; Output the cross-importance between nodes as indicator weights.

[0008] Furthermore, the method of performing status assessment on the actual data of the facility to obtain the attenuation factor includes: The actual data of the facilities are sorted in time series to obtain time series data, and the deep learning algorithm is used to extract the state features of the time series data. When the state has not changed, it is regarded as a fixed state feature, otherwise it is regarded as a changing state feature; Predict the damage extent of infrastructure based on the characteristics of the changing state and calculate the predicted damage extent: , The i-th change state characteristic at the s-th moment is , the i-th change state feature at the s+1th moment is, and the number of change state features is , the i-th change state feature is , the actual loss degree is L, and the probability of the change state characteristics and the actual loss degree appearing at the same time is , the probability of the change state feature appearing is , the probability of the actual loss level occurring is , the step function is ; Calculate the attenuation factor: , The attenuation factor is , the Euclidean norm is , the genetic factor is , the error coefficient is , the upper limit of the observation time is , the actual damage extent from time s to time s+1 is .

[0009] Furthermore, the method for constructing a quality evaluation function according to the indicator weight and the attenuation factor includes: The test data is screened according to the time series variation, and the test data with a time series variation greater than 0.217 is used as the evaluation index of infrastructure quality. The evaluation index is predicted based on the actual data of the facilities to obtain the predicted evaluation index; Construct the quality evaluation function, the expression is: , The attenuation factor is , the dth evaluation index , the dth prediction evaluation index is , evaluation index The indicator weight is , the number of evaluation indicators is , the quality evaluation function of the cth test is , the actual damage extent of the cth test is , the initial damage degree of the cth test is .

[0010] Furthermore, a method for constructing a quality infrastructure evaluation index system according to the quality evaluation function includes: The objective function is constructed according to the quality evaluation function, and the expression is: , , The loss function is , the quality evaluation of the cth test is , the objective function is , the quality evaluation function of the cth test is ; The quality infrastructure evaluation index system includes correlation screening algorithm, objective weighting method, and machine learning algorithm; The correlation screening algorithm obtains the quality evaluation index by screening according to the correlation between the test data and the infrastructure quality; The objective weighting method determines the weight of each indicator according to the correlation of quality evaluation indicators and obtains the indicator weight; The machine learning algorithm learns the evaluation model of the objective function, and based on the evaluation model, uses indicator weights and quality evaluation indicators to perform quality assessment and obtain a quality evaluation value.

[0011] Secondly, the quality infrastructure evaluation index system based on blockchain is constructed, including: Data collection module: used for collecting test data and actual data of infrastructure facilities, and preprocessing the test data and actual data of facilities; Extraction weighting module: used for performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; An evaluation construction module: used for performing a status evaluation on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; System construction module: used to construct a quality infrastructure evaluation indicator system according to the quality evaluation function and output a target system.

[0012] In a second aspect, an embodiment of the present application further provides an electronic device, including: A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0013] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0014] The beneficial effects of the present invention are: The present invention is a method and system for constructing a quality infrastructure evaluation index system based on blockchain. Compared with the prior art, the present invention has the following technical effects: The present invention can improve the accuracy of quality infrastructure evaluation index system construction through preprocessing, relationship extraction, directed weighting, state evaluation, construction of quality evaluation function and system construction steps, thereby improving the precision of quality infrastructure evaluation index system construction, optimizing the quality infrastructure evaluation index system construction, greatly saving resources, and improving work efficiency. It can realize the intelligent construction of the quality infrastructure evaluation index system, and perform directed weighting and state evaluation on the quality infrastructure evaluation index system in real time, which is of great significance to the construction of multiple quality infrastructure evaluation index systems, can adapt to the construction of quality infrastructure evaluation index systems of different standards and the construction requirements of different quality infrastructure evaluation index systems, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of the steps of the method for constructing a quality infrastructure evaluation index system based on blockchain in the present invention; Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0016] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0017] The method and system for constructing a quality infrastructure evaluation index system based on blockchain of the present invention include the following steps: like Figure 1 As shown, in this embodiment, the following steps are included: Collecting test data and actual facility data of the infrastructure, and preprocessing the test data and the actual facility data; In the actual evaluation, the water supply infrastructure of a central community in XXX city was taken as the research object, and the test data and actual facility data in 2023 were obtained; Test data includes water supply records, water quality test reports, pipeline maintenance records, water pressure, and water volume; Performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; In the actual assessment, the relationship between water pressure, water quantity and water quality, as well as the relationship between water pressure, water quantity and water quality and water supply stability are extracted to obtain the uncertainty of water pressure, water quantity and water quality; the directional weights of water pressure, water quantity and water quality are 0.3943, 0.4403 and 0.1654 respectively; Performing a status assessment on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; In the actual evaluation, the attenuation factor is 0.3125; A quality infrastructure evaluation index system is constructed according to the quality evaluation function, and a target system is output.

[0018] In this embodiment, the method of performing relationship extraction on the test data to obtain data relationship and uncertainty includes: The test data is input into the relation extraction model, and the bidirectional long short-term memory network is used to capture the correlation between the test data; Calculate the correlation: , The wth test data is , the fth test data is , test data and test data The correlation is , test data The number of relevant test data is , transposed to T, the weight parameter is , weight vector is u, test data and test data The correlation degree is , The test data with correlation greater than 0.278 are stored in pairs to obtain a correlation set; Split the test data into entities, use the test data entities as nodes, and express the nodes as follows: , Among them, the entity The corresponding node is ,entity The corresponding node is , node at layer m-1 The output of the convolutional layer is , node at layer m The weight matrix is , node at layer m The bias matrix is ; Connect the nodes according to the level and calculate the relationship coefficient: , Among them, the entity and entities The relationship embedding is ,entity and entities The relation coefficient of relation embedding is , the adjustment factor is , the activation function is , spliced ​​into ; The relationship extraction result is given according to the node representation and relationship coefficient. The expression is: , Among them, the entity and entities The relationship is The training parameters are , , the splicing entity is U, entity The weight matrix is ,entity The weight matrix is ,entity The bias matrix is ,entity The bias matrix is ; The uncertainty of random error is calculated based on the test data, and the uncertainty and relationship extraction results are output.

[0019] In this embodiment, the method of performing directed weighting according to the data relationship and the uncertainty to obtain the indicator weight includes: Construct a directed weighted network based on data relationships and uncertainties, use test data as network nodes, and connect network nodes based on data relationships; Given the loss function of a directed weighted network: , The input vector of node a is , the output vector of node a is , node a encoding layer is encoded as , the encoding layer of node x is encoded as , the edge weight between node a and node x is , the bias of node a is , the number of layers of the deep neural network encoder is , the encoder parameters of the zth layer are , the decoder parameters of the zth layer are , the Hadamard product is ; Calculate edge weights based on the association between nodes: , The edge weight between node a and node x is , the b-th data relation of node a is , the b-th data relation of node x is , the input vector of node x is , the association between node a and node x is ; Calculate the cross importance of nodes: , , , , , The i-th node is , the weight indicator function is , the node set is A, and the cross-regulation factor is ,node The output structural entropy is ,node The structural entropy of the input or output is ,node The cross-importance of ,node The number of k-th order connection points of input or output is , the maximum value function is , the minimum function is ,node The uncertainty is ; Output the cross-importance between nodes as indicator weights.

[0020] In this embodiment, the method of performing status evaluation on the actual data of the facility to obtain the attenuation factor includes: The actual data of the facilities are sorted in time series to obtain time series data, and the deep learning algorithm is used to extract the state features of the time series data. When the state has not changed, it is regarded as a fixed state feature, otherwise it is regarded as a changing state feature; Predict the damage extent of infrastructure based on the characteristics of the changing state and calculate the predicted damage extent: , The i-th change state characteristic at the s-th moment is , the i-th change state feature at the s+1th moment is, and the number of change state features is , the i-th change state feature is , the actual loss degree is L, and the probability of the change state characteristics and the actual loss degree appearing at the same time is , the probability of the change state feature appearing is , the probability of the actual loss level occurring is , the step function is ; Calculate the attenuation factor: , The attenuation factor is , the Euclidean norm is , the genetic factor is , the error coefficient is , the upper limit of the observation time is , the actual damage extent from time s to time s+1 is .

[0021] In this embodiment, the method for constructing a quality evaluation function according to the indicator weight and the attenuation factor includes: The test data is screened according to the time series variation, and the test data with a time series variation greater than 0.217 is used as the evaluation index of infrastructure quality. The evaluation index is predicted based on the actual data of the facilities to obtain the predicted evaluation index; Construct the quality evaluation function, the expression is: , The attenuation factor is , the dth evaluation index , the dth prediction evaluation index is , evaluation index The indicator weight is , the number of evaluation indicators is , the quality evaluation function of the cth test is , the actual damage extent of the cth test is , the initial damage degree of the cth test is .

[0022] In this embodiment, the method for constructing a quality infrastructure evaluation index system according to the quality evaluation function includes: The objective function is constructed according to the quality evaluation function, and the expression is: , , The loss function is , the quality evaluation of the cth test is , the objective function is , the quality evaluation function of the cth test is ; The quality infrastructure evaluation index system includes correlation screening algorithm, objective weighting method, and machine learning algorithm; The correlation screening algorithm obtains the quality evaluation index by screening according to the correlation between the test data and the infrastructure quality; The objective weighting method determines the weight of each indicator according to the correlation of quality evaluation indicators and obtains the indicator weight; The machine learning algorithm learns the evaluation model of the objective function, and based on the evaluation model, uses indicator weights and quality evaluation indicators to perform quality assessment and obtain a quality evaluation value.

[0023] Secondly, the quality infrastructure evaluation index system based on blockchain is constructed, including: Data collection module: used for collecting test data and actual data of infrastructure facilities, and preprocessing the test data and actual data of facilities; Extraction weighting module: used for performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; An evaluation construction module: used for performing a status evaluation on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; System construction module: used to construct a quality infrastructure evaluation index system according to the quality evaluation function and output a target system.

[0024] Figure 2This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0025] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0026] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0027] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a quality infrastructure evaluation index system construction device based on blockchain at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned quality infrastructure evaluation index system construction methods based on blockchain.

[0028] The above application Figure 1The method for constructing a quality infrastructure evaluation index system based on blockchain disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0029] The electronic device may also perform Figure 1 The method of constructing a quality infrastructure evaluation index system based on blockchain is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0030] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute any of the aforementioned blockchain-based quality infrastructure evaluation index system construction methods.

[0031] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt 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.

[0032] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0033] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0035] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0036] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0037] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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, 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. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0038] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0039] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take 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.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a quality infrastructure evaluation index system based on blockchain, characterized in that: The following steps are involved: Collecting test data and actual facility data of the infrastructure, and preprocessing the test data and the actual facility data; Performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; Performing a status assessment on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; A quality infrastructure evaluation index system is constructed according to the quality evaluation function, and a target system is output.

2. The method for constructing a quality infrastructure evaluation index system based on blockchain according to claim 1 is characterized in that: The method of performing relationship extraction on the test data to obtain data relationship and uncertainty includes: The test data is input into the relation extraction model, and the bidirectional long short-term memory network is used to capture the correlation between the test data; Calculate the correlation: ， The wth test data is , the fth test data is , test data and test data The correlation is , test data The number of relevant test data is , transposed to T, the weight parameter is , weight vector is u, test data and test data The correlation degree is , The test data with correlation greater than 0.278 are stored in pairs to obtain a correlation set; Split the test data into entities, use the test data entities as nodes, and express the nodes as follows: ， Among them, the entity The corresponding node is ,entity The corresponding node is , node at layer m-1 The output of the convolutional layer is , node at layer m The weight matrix is , node at layer m The bias matrix is ; Connect the nodes according to the level and calculate the relationship coefficient: ， Among them, the entity and entities The relationship embedding is ,entity and entities The relation coefficient of relation embedding is , the adjustment factor is , the activation function is , spliced ​​into ; The relationship extraction result is given according to the node representation and relationship coefficient. The expression is: ， Among them, the entity and entities The relationship is The training parameters are , , the splicing entity is U, entity The weight matrix is ,entity The weight matrix is ,entity The bias matrix is ,entity The bias matrix is ; The uncertainty of random error is calculated based on the test data, and the uncertainty and relationship extraction results are output.

3. According to the method for constructing a quality infrastructure evaluation index system based on blockchain in claim 1, it is characterized in that: The method for obtaining the indicator weight by performing directed weighting according to the data relationship and the uncertainty includes: Construct a directed weighted network based on data relationships and uncertainties, use test data as network nodes, and connect network nodes based on data relationships; Given the loss function of a directed weighted network: ， The input vector of node a is , the output vector of node a is , node a encoding layer is encoded as , the encoding layer of node x is encoded as , the edge weight between node a and node x is , the bias of node a is , the number of layers of the deep neural network encoder is , the encoder parameters of the zth layer are , the decoder parameters of the zth layer are , the Hadamard product is ; Calculate edge weights based on the association between nodes: ， The edge weight between node a and node x is , the b-th data relation of node a is , the b-th data relation of node x is , the input vector of node x is , the association between node a and node x is ; Calculate the cross importance of nodes: ， ， ， ， ， The i-th node is , the weight indicator function is , the node set is A, and the cross-regulation factor is ,node The output structural entropy is ,node The structural entropy of the input or output is ,node The cross-importance of ,node The number of k-th order connection points of input or output is , the maximum value function is , the minimum function is ,node The uncertainty is ; Output the cross-importance between nodes as indicator weights.

4. The method for constructing a quality infrastructure evaluation index system based on blockchain according to claim 1 is characterized in that: The method for performing status assessment on the actual data of the facility to obtain the attenuation factor comprises: The actual data of the facilities are sorted in time series to obtain time series data, and the deep learning algorithm is used to extract the state features of the time series data. When the state has not changed, it is regarded as a fixed state feature, otherwise it is regarded as a changing state feature; Predict the damage extent of infrastructure based on the characteristics of the changing state and calculate the predicted damage extent: ， The i-th change state characteristic at the s-th moment is , the i-th change state feature at the s+1th moment is, and the number of change state features is , the i-th change state feature is , the actual loss degree is L, and the probability of the change state characteristics and the actual loss degree appearing at the same time is , the probability of the change state feature appearing is , the probability of the actual loss level occurring is , the step function is ; Calculate the attenuation factor: ， The attenuation factor is , the Euclidean norm is , the genetic factor is , the error coefficient is , the upper limit of the observation time is , the actual damage extent from time s to time s+1 is .

5. The method for constructing a quality infrastructure evaluation index system based on blockchain according to claim 1 is characterized in that: The method for constructing a quality evaluation function according to the indicator weight and the attenuation factor comprises: The test data is screened according to the time series variation, and the test data with a time series variation greater than 0.217 is used as the evaluation index of infrastructure quality. The evaluation index is predicted based on the actual data of the facilities to obtain the predicted evaluation index; Construct the quality evaluation function, the expression is: ， The attenuation factor is , the dth evaluation index , the dth prediction evaluation index is , evaluation index The indicator weight is , the number of evaluation indicators is , the quality evaluation function of the cth test is , the actual damage extent of the cth test is , the initial damage degree of the cth test is .

6. The method for constructing a quality infrastructure evaluation index system based on blockchain according to claim 1 is characterized in that: The method for constructing a quality infrastructure evaluation index system according to the quality evaluation function comprises: The objective function is constructed according to the quality evaluation function, and the expression is: ， ， The loss function is , the quality evaluation of the cth test is , the objective function is , the quality evaluation function of the cth test is ; The quality infrastructure evaluation index system includes correlation screening algorithm, objective weighting method, and machine learning algorithm; The correlation screening algorithm obtains the quality evaluation index by screening according to the correlation between the test data and the infrastructure quality; The objective weighting method determines the weight of each indicator according to the correlation of quality evaluation indicators and obtains the indicator weight; The machine learning algorithm learns the evaluation model of the objective function, and based on the evaluation model, uses indicator weights and quality evaluation indicators to perform quality assessment and obtain a quality evaluation value.

7. A system for constructing a quality infrastructure evaluation index system based on blockchain, for executing the method described in any one of claims 1 to 6, characterized in that: include: Data collection module: used for collecting test data and actual data of infrastructure facilities, and preprocessing the test data and actual data of facilities; Extraction weighting module: used for performing relationship extraction on the test data to obtain data relationship and uncertainty, and performing directed weighting according to the data relationship and the uncertainty to obtain indicator weight; An evaluation construction module: used for performing a status evaluation on the actual data of the facility to obtain an attenuation factor, and constructing a quality evaluation function according to the indicator weight and the attenuation factor; System construction module: used to construct a quality infrastructure evaluation index system according to the quality evaluation function and output a target system.

8. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 6.

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