Multi-terminal data interaction method and system based on rail transit

Abstract

The present invention relates to the technical field of multi-terminal data interaction, and discloses a multi-terminal data interaction method and system based on rail transit, including: creating a communication token between a sending end and a receiving end, identifying a service module in a receiving end, extracting module data in each module in a service module, obtaining interaction data corresponding to a data interaction request, analyzing a request purpose corresponding to a data interaction request, and determining the data to be interacted corresponding to the data interaction request from the module data; calculating the support between the module data and the data to be interacted, obtaining the data support, querying the module corresponding to the data to be interacted in the service module, obtaining an interaction module, performing data normalization processing on the data to be interacted, obtaining normalized data, performing data integration on the associated interaction data and the normalized data, obtaining integrated interaction data, and executing data interaction corresponding to the interaction request. The present invention aims to improve the interaction accuracy of multi-terminal data based on rail transit.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-terminal data interaction, and in particular to a multi-terminal data interaction method and system based on rail transit. Background Art

[0002] Multi-terminal data interaction usually refers to the mutual transmission and sharing of data between different server ends. In computers, data interaction can be carried out through network protocols, APIs (application programming interfaces), etc. Specifically, data interaction may include the following aspects: data upload, data download, data synchronization, data sharing, etc. There are many application areas, such as rail transit. When the subway in rail transit needs to perform fault detection, data sharing, transmission and interaction can be carried out between multiple terminals in rail transit, which can improve the efficiency of fault detection.

[0003] However, the existing multi-terminal data interaction method for rail transit is mainly to establish a communication link between the server and the server of rail transit, then locate the data resources for interaction through the URL, and then schedule the data resources for interaction through methods such as GET, POST and PUT. However, this method increases the amount of data calculation by the computer, and the scheduled data resources only consider the relationship with the interactive data, resulting in incomplete interactive data, which in turn reduces the interaction accuracy of multi-terminal data of rail transit. Therefore, a method is needed to improve the interaction accuracy of multi-terminal data of rail transit. Summary of the invention

[0004] The present invention provides a multi-terminal data interaction method and system based on rail transit, the main purpose of which is to improve the interaction accuracy of multi-terminal data of rail transit.

[0005] To achieve the above-mentioned purpose, the present invention provides a multi-terminal data interaction method based on rail transit, comprising:

[0006] Acquire a data management platform applied to rail transit management, the data management platform comprising: a sending end and a receiving end;

[0007] Before receiving the data interaction request sent by the sending end, creating a communication token between the sending end and the receiving end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token;

[0008] Identify the service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze the request purpose corresponding to the data interaction request according to the interaction data, and determine the to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data;

[0009] The support between the module data and the data to be interacted is calculated by the following formula to obtain the data support;

[0010]

[0011] Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, and M n Represents the basic probability value corresponding to the nth module data, P n+1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted;

[0012] According to the data support, data associated with the to-be-interacted data is screened out from the module data to obtain associated interaction data;

[0013] The module corresponding to the data to be interacted with in the service module is queried to obtain an interaction module, and an interaction conflict coefficient between the interaction modules is calculated; if the interaction conflict coefficient is greater than a preset conflict coefficient, data normalization is performed on the data to be interacted with to obtain normalized data; data integration is performed on the associated interaction data and the normalized data to obtain integrated interaction data; and data interaction corresponding to the interaction request is executed through the integrated interaction data.

[0014] Optionally, the creating a communication token between the sending end and the receiving end includes:

[0015] Identify the port protocols corresponding to the sending end and the receiving end, and obtain a sending port protocol and a receiving port protocol;

[0016] Extracting instructions corresponding to the sending port protocol and the receiving port protocol to obtain a sending port instruction and a receiving port instruction, analyzing a mapping relationship between the sending port instruction and the receiving port instruction to obtain a port mapping relationship;

[0017] The communication link between the sending end and the receiving end is obtained, a link attribute corresponding to the communication link is queried, and a communication token between the sending end and the receiving end is created according to the link attribute and the port mapping relationship.

[0018] Optionally, analyzing, according to the interaction data, a request purpose corresponding to the data interaction request includes:

[0019] Extracting a label corresponding to each data in the interactive data to obtain a data label, and calculating a label weight corresponding to the data label;

[0020] According to the tag weight, the data tag is subjected to tag filtering to obtain a target data tag, and the text information corresponding to the target data tag and the data interaction request is extracted respectively to obtain a tag text and a request text;

[0021] Semantic parsing is performed on the label text and the request text respectively to obtain label semantics and request semantics, and the request purpose corresponding to the data interaction request is analyzed according to the label semantics and the request semantics.

[0022] Optionally, the determining, in combination with the request purpose and the interaction data, the to-be-interacted data corresponding to the data interaction request from the module data includes:

[0023] Performing attribute parsing on the request purpose and the interaction data respectively to obtain a first attribute and a second attribute, and constructing matrices corresponding to the first attribute and the second attribute respectively to obtain a first matrix and a second matrix;

[0024] Performing matrix fusion on the first matrix and the second matrix to obtain a fusion matrix, performing attribute analysis on each data in the to-be-interacted data to obtain a third attribute, and constructing a matrix corresponding to the third attribute to obtain a third matrix;

[0025] Calculate the matrix similarity between the fusion matrix and the third matrix, and when the matrix similarity is greater than a preset similarity, determine the to-be-interacted data corresponding to the data interaction request from the module data.

[0026] Optionally, the calculating the matrix similarity between the fusion matrix and the third matrix includes:

[0027] The matrix similarity between the fusion matrix and the third matrix is ​​calculated by the following formula:

[0028]

[0029] Among them, F represents the matrix similarity between the fusion matrix and the third matrix, E represents the matrix set of the fusion matrix and the third matrix, e represents the value of the matrix set, G i，e Indicates the ratio of the fusion matrix in the matrix set, represents the average ratio of the fusion matrix in the matrix set, G i+1，e represents the ratio of the third matrix in the matrix set, Represents the average ratio of the third matrix in the matrix set.

[0030] Optionally, the filtering out data associated with the to-be-interacted data from the module data according to the data support degree includes:

[0031] According to the data support, the number of times each data in the module data supports the data to be interacted with is measured;

[0032] Calculating the confidence of each data and the data to be interacted with according to the number of supports to obtain data confidence;

[0033] Performing a weighted summation on the data confidence and the data support to obtain a correlation coefficient between each data and the data to be interacted;

[0034] According to the correlation coefficient, data associated with the data to be interacted with is screened out from the module data.

[0035] Optionally, the calculating the interaction conflict coefficient between the interaction modules includes:

[0036] The interaction conflict coefficient between the interaction modules is calculated by the following formula:

[0037]

[0038] Where Q(j, j+1) represents the interaction conflict coefficient between the jth interaction module and the j+1th interaction module, j and j+1 represent the sequence numbers of the interaction modules, z represents the total number of interaction modules, and R j represents the number of conflicts corresponding to the jth interaction module, T j+1 represents the number of conflicts corresponding to the j+1th interaction module, and Π represents the product operator symbol.

[0039] Optionally, performing data normalization processing on the to-be-interacted data to obtain normalized data includes:

[0040] Performing linear processing on the data to be interacted with to obtain data linear values ​​corresponding to the data to be interacted with, and performing scaling processing on each data in the data to be interacted with according to the data linear values ​​to obtain scaled interaction data;

[0041] Querying the interaction rules corresponding to each module in the interaction module, assigning weights to the interaction rules to obtain rule weight values, and integrating the interaction rules according to the rule weight values ​​to obtain target interaction rules;

[0042] According to the target interaction rule, the scaled interaction data is normalized using a preset normalization method to obtain normalized data.

[0043] Optionally, scaling each data in the to-be-interacted data according to the data linear value to obtain scaled interactive data includes:

[0044] Each data in the to-be-interacted data is scaled using the following formula:

[0045]

[0046] Among them, x new represents the scaled interactive data obtained after scaling, x represents the original data corresponding to the interactive data, and x max Indicates the linear maximum value in the data linear value, x min Represents the linear minimum among the linear values ​​of the data.

[0047] In order to solve the above problems, the present invention also provides a multi-terminal data interaction system based on rail transit, the system comprising:

[0048] A management platform acquisition module is used to acquire a data management platform applied to rail transit management, wherein the data management platform includes: a sending end and a receiving end;

[0049] A communication token building module, used for creating a communication token between the sending end and the receiving end before receiving the data interaction request sent by the sending end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token;

[0050] an interaction data determination module, configured to identify a service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze a request purpose corresponding to the data interaction request according to the interaction data, and determine to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data;

[0051] A support calculation module, used to calculate the support between the module data and the data to be interacted with, to obtain data support;

[0052] A related data screening module, used for screening data related to the to-be-interacted data from the module data according to the data support, to obtain related interaction data;

[0053] A data interaction module is used to query the module corresponding to the data to be interacted in the service module to obtain the interaction module, calculate the interaction conflict coefficient between the interaction modules, and if the interaction conflict coefficient is greater than a preset conflict coefficient, perform data normalization processing on the data to be interacted to obtain normalized data, perform data integration on the associated interaction data and the normalized data to obtain integrated interaction data, and execute the data interaction corresponding to the interaction request through the integrated interaction data.

[0054] The present invention obtains a data management platform applied to rail transit management, and can understand multiple ports in the platform through the data management platform to facilitate subsequent multi-end data interaction. The present invention creates a communication token between the sending end and the receiving end, which can facilitate verification of subsequent data multi-end interaction and improve the reliability and accuracy of data interaction. The present invention identifies the service module in the receiving end and extracts the module data in each module in the service module, which facilitates the extraction of data in the service module and the subsequent determination of the data to be interacted. The present invention calculates the support between the module data and the data to be interacted. Understand the degree of support of each data in the module data for the interactive data, so as to facilitate the subsequent data interaction to obtain more comprehensive data; in addition, the present invention can improve the comprehensiveness of the data during the interaction by screening out the data associated with the data to be interacted from the module data according to the data support, and obtain the interactive module by querying the module corresponding to the data to be interacted in the service module, and calculate the interactive conflict coefficient between the interactive modules. The difficulty of data interaction between the interactive modules can be understood through the interactive conflict coefficient, and then determine whether the subsequent generation of interactive data needs to be normalized. Therefore, the multi-terminal data interaction method and system based on rail transit provided by the embodiment of the present invention can improve the accuracy of multi-terminal data interaction based on rail transit. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a flow chart of a multi-terminal data interaction method based on rail transit provided by an embodiment of the present invention;

[0056] Figure 2 A functional module diagram of a multi-terminal data interaction system based on rail transit provided by an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of the structure of an electronic device for implementing the rail transit-based multi-terminal data interaction method provided in one embodiment of the present invention.

[0058] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0060] The embodiment of the present application provides a multi-terminal data interaction method based on rail transit. In the embodiment of the present application, the execution subject of the multi-terminal data interaction method based on rail transit includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the multi-terminal data interaction method based on rail transit can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0061] Reference Figure 1 FIG. 1 is a flow chart of a multi-terminal data interaction method based on rail transit provided by an embodiment of the present invention. In this embodiment, the multi-terminal data interaction method based on rail transit includes steps S1-S6.

[0062] S1. Acquire a data management platform applied to rail transit management, wherein the data management platform includes: a transmitting end and a receiving end.

[0063] The present invention obtains a data management platform applied to rail transit management, and can understand multiple ports in the platform through the data management platform to facilitate subsequent multi-terminal data interaction, wherein the data management platform is a platform for managing traffic data in the rail transit, the sending end is a sending process in the data management platform for sending some requests, and the receiving end is a request process in the data management platform for receiving the sending end.

[0064] S2. Before receiving the data interaction request sent by the sending end, create a communication token between the sending end and the receiving end, and based on the communication token, transmit the data interaction request in the sending end to the receiving end.

[0065] The present invention can facilitate the verification of subsequent data during multi-terminal interaction by creating a communication token between the sending end and the receiving end, thereby improving the reliability and accuracy of data interaction, wherein the data interaction request is an instruction for the data sent by the sending end to interact, and the communication token is the corresponding verification program when the sending end and the receiving end interact with each other.

[0066] As an embodiment of the present invention, the creation of a communication token between the sending end and the receiving end includes: identifying the port protocols corresponding to the sending end and the receiving end, obtaining the sending port protocol and the receiving port protocol, extracting the instructions corresponding to the sending port protocol and the receiving port protocol, obtaining the sending port instructions and the receiving port instructions, analyzing the mapping relationship between the sending port instructions and the receiving port instructions, obtaining the port mapping relationship, acquiring the communication link between the sending end and the receiving end, querying the link attributes corresponding to the communication link, and creating the communication token between the sending end and the receiving end according to the link attributes and the port mapping relationship.

[0067] Among them, the sending port protocol is the agreement or communication rule designed when the sending end communicates with the receiving end, the receiving port protocol is the communication rule for the receiving end to receive the request or instruction sent by the sending end, the sending port instruction is the code for the sending port protocol to send the request, the receiving port instruction is the code corresponding to the request received by the receiving port protocol, the port mapping relationship is the corresponding relationship between the sending end and the receiving end, the communication link is the corresponding transmission channel when the sending end and the receiving end exchange data, and the link attribute is the attribute information corresponding to the communication link.

[0068] Further, as an optional embodiment of the present invention, identifying the port protocols corresponding to the sending end and the receiving end can be achieved by a port identification tool, which is compiled by a scripting language. Extracting the instructions corresponding to the sending port protocol and the receiving port protocol can be achieved by an instruction extractor, which is compiled by Java language. Analyzing the mapping relationship between the sending port instructions and the receiving port instructions can be achieved by a mapping function, such as a char function. Acquiring the communication link between the sending end and the receiving end can be achieved by a network monitoring tool, such as a Prometheus tool. Querying the link attributes corresponding to the communication link can be achieved by an SQL query method. Creating a communication token between the sending end and the receiving end can be achieved by combining the unique relationship between the link attributes and the port mapping relationship to create a communication token between the sending end and the receiving end.

[0069] The present invention transmits the data interaction request in the sending end to the receiving end based on the communication token. After the communication token is successfully verified, the data interaction request in the sending end can be transmitted to the receiving end, thereby improving the accuracy of data interaction.

[0070] S3. Identify the service module in the receiving end, extract the module data in each module in the service module, obtain the interaction data corresponding to the data interaction request, analyze the request purpose corresponding to the data interaction request based on the interaction data, and determine the data to be interacted corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data.

[0071] The present invention extracts module data from each module in the service module by identifying the service module in the receiving end, thereby facilitating the extraction of data in the service module and subsequent determination of data to be interacted with, wherein the service module is each service unit in the receiving end, and the module data is data stored in the service module. Furthermore, the identification of the service module in the receiving end can be achieved through an identification function, such as a K-NN algorithm, and the extraction of module data from each module in the service module can be achieved through an extraction function, such as a left function.

[0072] The present invention obtains the interaction data corresponding to the data interaction request, analyzes the request purpose corresponding to the data interaction request based on the interaction data, and can understand the desired result of the data interaction request through the request purpose, wherein the interaction data is the original data that needs to be interacted with by the data interaction request, and the request purpose is the purpose that the data interaction request needs to achieve after interacting with the interaction data.

[0073] As an embodiment of the present invention, analyzing the request purpose corresponding to the data interaction request based on the interaction data includes: extracting the label corresponding to each data in the interaction data to obtain the data label, calculating the label weight corresponding to the data label, performing label filtering on the data label according to the label weight to obtain the target data label, respectively extracting the text information corresponding to the target data label and the data interaction request to obtain the label text and the request text, respectively performing semantic analysis on the label text and the request text to obtain the label semantics and the request semantics, and analyzing the request purpose corresponding to the data interaction request according to the label semantics and the request semantics.

[0074] Among them, the data label is the identifier corresponding to each data in the interactive data, the label weight indicates the importance of the data label, the target data label is the label obtained after filtering the data label according to the numerical value of the label weight, the label text and the request text are the text information corresponding to the target data label and the data interaction request respectively, and the label semantics and the request semantics are the meaning and explanation of the label text and the request text respectively.

[0075] Further, as an optional embodiment of the present invention, extracting the label corresponding to each data in the interactive data can be achieved by a label extractor, label filtering of the data labels can be performed by a DFA algorithm, respectively extracting the target data label and the text information corresponding to the data interaction request can be achieved by an OCR text recognition algorithm, respectively performing semantic analysis on the label text and the request text can be achieved by a semantic differentiation method, and analyzing the request purpose corresponding to the data interaction request can be achieved by mining the semantic features corresponding to the label semantics and the request semantics, and obtaining the corresponding request purpose based on the semantic features.

[0076] Further, as an optional embodiment of the present invention, the calculating the label weight corresponding to the data label includes:

[0077] The label weight corresponding to the data label is calculated by the following formula:

[0078]

[0079] Among them, D(a, a+1) represents the label weights corresponding to the a-th data label and the a+1-th data label, a and a+1 represent the serial numbers of the data labels respectively, and B a represents the mapping value of the ath data label, C a+1 It represents the mapping value of the a+1th data label, and α represents the label dimension corresponding to the data label.

[0080] The present invention determines the data to be interacted corresponding to the data interaction request from the module data by combining the request purpose and the interaction data, so as to improve the accuracy of subsequent data interaction, wherein the data to be interacted is the data in the module data that interacts with the interaction data.

[0081] As an embodiment of the present invention, the combination of the request purpose and the interaction data to determine the data to be interacted corresponding to the data interaction request from the module data includes: performing attribute parsing on the request purpose and the interaction data respectively to obtain a first attribute and a second attribute, respectively constructing matrices corresponding to the first attribute and the second attribute to obtain a first matrix and a second matrix, performing matrix fusion on the first matrix and the second matrix to obtain a fused matrix, performing attribute parsing on each data in the data to be interacted to obtain a third attribute, constructing a matrix corresponding to the third attribute to obtain a third matrix, calculating the matrix similarity between the fused matrix and the third matrix, and when the matrix similarity is greater than a preset similarity, determining the data to be interacted corresponding to the data interaction request from the module data.

[0082] Among them, the first attribute and the second attribute are respectively the basic properties corresponding to the request purpose and the interaction data, the first matrix and the second matrix are respectively the square matrices corresponding to the first attribute and the second attribute, the fusion matrix is ​​the matrix obtained by fusing the first matrix and the second matrix, the third attribute is the basic property corresponding to each data in the data to be interacted, the third matrix is ​​the square matrix corresponding to each attribute in the third attribute, the matrix similarity represents the similarity between the fusion matrix and the third matrix, the preset similarity is the standard for judging the matrix similarity, which can be 0.8, and can also be set according to the actual business scenario.

[0083] Further, as an optional embodiment of the present invention, attribute parsing of the request purpose and the interaction data respectively can be achieved by an attribute parsing tool, the attribute parsing tool is compiled by a scripting language, constructing matrices corresponding to the first attribute and the second attribute respectively can be achieved by matrix construction functions, such as the zeros function, matrix fusion of the first matrix and the second matrix can be achieved by the Voting-K-Means algorithm, attribute parsing of each data in the data to be interacted can be achieved by the above-mentioned attribute parsing tool, and constructing the matrix corresponding to the third attribute can be achieved by the above-mentioned zeros function.

[0084] Further, as an optional embodiment of the present invention, the calculating the matrix similarity between the fusion matrix and the third matrix includes:

[0085] The matrix similarity between the fusion matrix and the third matrix is ​​calculated by the following formula:

[0086]

[0087] Among them, F represents the matrix similarity between the fusion matrix and the third matrix, E represents the matrix set of the fusion matrix and the third matrix, e represents the value of the matrix set, G i，e Indicates the ratio of the fusion matrix in the matrix set, represents the average ratio of the fusion matrix in the matrix set, Gi+1,e represents the ratio of the third matrix in the matrix set, Represents the average ratio of the third matrix in the matrix set.

[0088] S4. Calculate the support between the module data and the data to be interacted with to obtain data support.

[0089] By calculating the support between the module data and the data to be interacted, the present invention can understand the degree of support of each data in the module data for the interactive data, thereby facilitating obtaining more comprehensive data in subsequent data interaction, wherein the data support is the degree of support of the module data for the data to be interacted.

[0090] As an embodiment of the present invention, the calculating the support between the module data and the to-be-interacted data to obtain the data support includes:

[0091]

[0092] Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, and M n Represents the basic probability value corresponding to the nth module data, P n+1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted.

[0093] S5. According to the data support, data associated with the to-be-interacted data is screened out from the module data to obtain associated interaction data.

[0094] The present invention can improve the comprehensiveness of data during interaction by screening out data associated with the data to be interacted from the module data according to the data support, wherein the associated interaction data is the.

[0095] As an embodiment of the present invention, the method of screening out data associated with the data to be interacted from the module data based on the data support includes: measuring the number of supports between each data in the module data and the data to be interacted based on the data support, calculating the confidence between each data and the data to be interacted based on the support number to obtain data confidence, performing weighted summation of the data confidence and the data support to obtain a correlation coefficient between each data and the data to be interacted, and screening out data associated with the data to be interacted from the module data based on the correlation coefficient.

[0096] Among them, the support number is the number of support relationships between each data in the module data and the data to be interacted. For example, module data one has support with data one to be interacted, data two to be interacted and data three to be interacted, then the support number of module data one is 3. The data confidence is the reliability between each data in the module data and the data to be interacted. The association coefficient is obtained by weighted summing the data confidence and the association coefficient, which represents the degree of association between the data.

[0097] Furthermore, the frequency statistical analysis method can be used to measure the number of times each data in the module data supports the data to be interacted with, the Bootstrap method can be used to calculate the confidence of each data and the data to be interacted with, the weighted sum of the data confidence and the data support can be achieved through a function method, such as the SUMPRODUCT function, and the filtering of data associated with the data to be interacted with from the module data can be achieved through a filtering function, such as the FILTER function.

[0098] S6. Query the module corresponding to the data to be interacted in the service module to obtain the interaction module, calculate the interaction conflict coefficient between the interaction modules, and if the interaction conflict coefficient is greater than the preset conflict coefficient, perform data normalization processing on the data to be interacted to obtain normalized data, perform data integration on the associated interaction data and the normalized data to obtain integrated interaction data, and execute the data interaction corresponding to the interaction request through the integrated interaction data.

[0099] The present invention obtains the interaction module by querying the module corresponding to the data to be interacted in the service module, and calculates the interaction conflict coefficient between the interaction modules. The difficulty of data interaction between the interaction modules can be understood through the interaction conflict coefficient, and then it can be judged whether the subsequent operation of normalizing the generation of interaction data is required, wherein the interaction module is a module unit in the service module that runs independently and has different functions, and the interaction conflict coefficient represents the difficulty of data interaction between the interaction modules. Furthermore, querying the module corresponding to the data to be interacted in the service module can be implemented by a query function, such as a find function.

[0100] As an embodiment of the present invention, the calculating the interaction conflict coefficient between the interaction modules includes:

[0101] The interaction conflict coefficient between the interaction modules is calculated by the following formula:

[0102]

[0103] Where Q(j, j+1) represents the interaction conflict coefficient between the jth interaction module and the j+1th interaction module, j and j+1 represent the sequence numbers of the interaction modules, z represents the total number of interaction modules, and R j represents the number of conflicts corresponding to the jth interaction module, T j+1 represents the number of conflicts corresponding to the j+1th interaction module, and Π represents the product operator symbol.

[0104] It should be understood that if the interaction conflict coefficient is greater than the preset conflict coefficient, it means that there is difficulty in data interaction between the interaction modules. The present invention normalizes the data to be interacted with so as to reduce the difficulty of data interaction, wherein the normalized data is the data obtained after the format and data structure corresponding to the associated data and the data to be interacted are normalized.

[0105] As an embodiment of the present invention, the data normalization processing of the data to be interacted to obtain normalized data includes: performing linear processing on the data to be interacted to obtain data linear values ​​corresponding to the data to be interacted, scaling each data in the data to be interacted according to the data linear values ​​to obtain scaled interaction data, querying the interaction rules corresponding to each module in the interaction module, assigning weights to the interaction rules to obtain rule weight values, integrating the interaction rules according to the rule weight values ​​to obtain target interaction rules, and normalizing the scaled interaction data according to the target interaction rules using a preset normalization method to obtain normalized data.

[0106] Among them, the data linear value is the numerical expression of the linear relationship between each data in the data to be interacted and the position of the data in the data distribution, the scaled interaction data is the data obtained after scaling the data to be interacted, the interaction rule is the law and rule of interaction of each module in the interaction module, the rule weight value indicates the importance corresponding to the interaction rule, the target interaction rule is the data interaction rule obtained by integrating the interaction rules according to the numerical size of the rule weight value, and the preset normalization method is a method for normalizing the data, such as the inverse cotangent function method.

[0107] Furthermore, linear processing of the data to be interacted can be achieved through a linear function, such as a bilinear function, querying the interaction rules corresponding to each module in the interaction module can be achieved through an SQL query method, weight assignment of the interaction rules can be achieved through an entropy method, and rule integration of the interaction rules can be achieved through a stacking method.

[0108] Further, as an optional embodiment of the present invention, scaling each data in the to-be-interacted data according to the data linear value to obtain scaled interactive data includes:

[0109] Each data in the to-be-interacted data is scaled using the following formula:

[0110]

[0111] Among them, x new represents the scaled interactive data obtained after scaling, x represents the original data corresponding to the interactive data to be processed, and x max represents the linear maximum value among the linear values ​​of the data, x min Represents the linear minimum value among the linear values ​​of the data.

[0112] The present invention obtains integrated interaction data by integrating the associated interaction data and the normalized data. The data interaction corresponding to the interaction request is executed through the integrated interaction data, thereby completing the multi-terminal data interaction process of the data management platform. The efficiency of data interaction can be improved through the integrated interaction data, wherein the integrated interaction data is the data obtained after the associated interaction data and the normalized data are merged and integrated. Furthermore, the data integration of the associated interaction data and the normalized data can be achieved through an automated integration method.

[0113] The present invention obtains a data management platform applied to rail transit management, and can understand multiple ports in the platform through the data management platform to facilitate subsequent multi-end data interaction. The present invention creates a communication token between the sending end and the receiving end, which can facilitate verification of subsequent data multi-end interaction and improve the reliability and accuracy of data interaction. The present invention identifies the service module in the receiving end and extracts the module data in each module in the service module, which facilitates the extraction of data in the service module and the subsequent determination of the data to be interacted. The present invention calculates the support between the module data and the data to be interacted. Understand the degree of support of each data in the module data for the interactive data, so as to facilitate the subsequent data interaction to obtain more comprehensive data; in addition, the present invention can improve the comprehensiveness of the data during the interaction by screening out the data associated with the data to be interacted from the module data according to the data support, and obtain the interactive module by querying the module corresponding to the data to be interacted in the service module, and calculate the interactive conflict coefficient between the interactive modules. The difficulty of data interaction between the interactive modules can be understood through the interactive conflict coefficient, and then determine whether the subsequent generation of interactive data needs to be normalized. Therefore, the multi-terminal data interaction method based on rail transit provided in an embodiment of the present invention can improve the accuracy of multi-terminal data interaction based on rail transit.

[0114] like Figure 2 , which is a functional module diagram of a multi-terminal data interaction system based on rail transit provided by one embodiment of the present invention.

[0115] The multi-terminal data interaction system 100 based on rail transit of the present invention can be installed in an electronic device. According to the functions implemented, the multi-terminal data interaction system 100 based on rail transit can include a management platform acquisition module 101, a communication token construction module 102, an interactive data determination module 103, a support calculation module 104, an associated data screening module 105 and a data interaction module 106. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0116] In this embodiment, the functions of each module / unit are as follows:

[0117] The management platform acquisition module 101 is used to acquire a data management platform applied to rail transit management, and the data management platform includes: a sending end and a receiving end;

[0118] The communication token building module 102 is used to create a communication token between the sending end and the receiving end before receiving the data interaction request sent by the sending end, and transmit the data interaction request in the sending end to the receiving end based on the communication token;

[0119] The interactive data determination module 103 is used to identify the service module in the receiving end, extract the module data in each module in the service module, obtain the interactive data corresponding to the data interactive request, analyze the request purpose corresponding to the data interactive request according to the interactive data, and determine the to-be-interacted data corresponding to the data interactive request from the module data in combination with the request purpose and the interactive data;

[0120] The support calculation module 104 is used to calculate the support between the module data and the data to be interacted with, and obtain the data support;

[0121] The associated data screening module 105 is used to screen out data associated with the to-be-interacted data from the module data according to the data support, to obtain associated interaction data;

[0122] The data interaction module 106 is used to query the module corresponding to the data to be interacted in the service module, obtain the interaction module, calculate the interaction conflict coefficient between the interaction modules, and if the interaction conflict coefficient is greater than the preset conflict coefficient, perform data normalization processing on the data to be interacted to obtain normalized data, perform data integration on the associated interaction data and the normalized data to obtain integrated interaction data, and execute the data interaction corresponding to the interaction request through the integrated interaction data.

[0123] In detail, each module described in the multi-terminal data interaction system 100 based on rail transit in the embodiment of the present application is used in the same manner as described above. Figure 1 The same technical means are used as the multi-terminal data interaction method based on rail transit described in, and can produce the same technical effects, so they will not be repeated here.

[0124] like Figure 3 , is a schematic diagram of the structure of an electronic device 1 for implementing a multi-terminal data interaction method based on rail transit provided by an embodiment of the present invention.

[0125] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a multi-terminal data interaction method program based on rail transit.

[0126] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a multi-terminal data interaction method program based on rail transit, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0127] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a multi-terminal data interaction method program based on rail transit, but also can be used to temporarily store data that has been output or is to be output.

[0128] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0129] The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0130] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0131] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0132] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0133] The multi-terminal data interaction method program based on rail transit stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0134] Acquire a data management platform applied to rail transit management, the data management platform comprising: a sending end and a receiving end;

[0135] Before receiving the data interaction request sent by the sending end, creating a communication token between the sending end and the receiving end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token;

[0136] Identify the service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze the request purpose corresponding to the data interaction request according to the interaction data, and determine the to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data;

[0137] The support between the module data and the data to be interacted is calculated by the following formula to obtain the data support;

[0138]

[0139] Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, and M n Represents the basic probability value corresponding to the nth module data, P n+1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted;

[0140] According to the data support, data associated with the to-be-interacted data is screened out from the module data to obtain associated interaction data;

[0141] The module corresponding to the data to be interacted with in the service module is queried to obtain an interaction module, and an interaction conflict coefficient between the interaction modules is calculated; if the interaction conflict coefficient is greater than a preset conflict coefficient, data normalization is performed on the data to be interacted with to obtain normalized data; data integration is performed on the associated interaction data and the normalized data to obtain integrated interaction data; and data interaction corresponding to the interaction request is executed through the integrated interaction data.

[0142] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0143] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0144] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0145] Acquire a data management platform applied to rail transit management, the data management platform comprising: a sending end and a receiving end;

[0146] Before receiving the data interaction request sent by the sending end, creating a communication token between the sending end and the receiving end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token;

[0147] Identify the service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze the request purpose corresponding to the data interaction request according to the interaction data, and determine the to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data;

[0148] The support between the module data and the data to be interacted is calculated by the following formula to obtain the data support;

[0149]

[0150] Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, and M n Represents the basic probability value corresponding to the nth module data, P n+1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted;

[0151] According to the data support, data associated with the to-be-interacted data is screened out from the module data to obtain associated interaction data;

[0152] The module corresponding to the data to be interacted with in the service module is queried to obtain an interaction module, and an interaction conflict coefficient between the interaction modules is calculated; if the interaction conflict coefficient is greater than a preset conflict coefficient, data normalization is performed on the data to be interacted with to obtain normalized data; data integration is performed on the associated interaction data and the normalized data to obtain integrated interaction data; and data interaction corresponding to the interaction request is executed through the integrated interaction data.

[0153] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0154] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0156] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0158] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0159] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A multi-terminal data interaction method based on rail transit, characterized in that: The method comprises: Acquire a data management platform applied to rail transit management, the data management platform comprising: a sending end and a receiving end; Before receiving the data interaction request sent by the sending end, creating a communication token between the sending end and the receiving end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token; Identify the service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze the request purpose corresponding to the data interaction request according to the interaction data, and determine the to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data; The support between the module data and the data to be interacted is calculated by the following formula to obtain the data support; Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, Mn represents the basic probability value corresponding to the nth module data, Pn +1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted; According to the data support, data associated with the to-be-interacted data is screened out from the module data to obtain associated interaction data; Querying the module corresponding to the data to be interacted in the service module to obtain an interaction module, calculating the interaction conflict coefficient between the interaction modules, and if the interaction conflict coefficient is greater than a preset conflict coefficient, performing data normalization processing on the data to be interacted to obtain normalized data, performing data integration on the associated interaction data and the normalized data to obtain integrated interaction data, and executing data interaction corresponding to the interaction request through the integrated interaction data, wherein calculating the interaction conflict coefficient between the interaction modules includes: The interaction conflict coefficient between the interaction modules is calculated by the following formula: Where Q(j, j+1) represents the interaction conflict coefficient between the jth interaction module and the j+1th interaction module, j and j+1 represent the sequence numbers of the interaction modules, z represents the total number of interaction modules, Rj represents the number of conflicts corresponding to the jth interaction module, Tj +1 It represents the number of conflicts corresponding to the j+1th interaction module, and ∏ represents the product operator symbol.

2. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The creating a communication token between the sending end and the receiving end includes: Identify the port protocols corresponding to the sending end and the receiving end, and obtain a sending port protocol and a receiving port protocol; Extracting instructions corresponding to the sending port protocol and the receiving port protocol to obtain a sending port instruction and a receiving port instruction, analyzing a mapping relationship between the sending port instruction and the receiving port instruction to obtain a port mapping relationship; The communication link between the sending end and the receiving end is obtained, a link attribute corresponding to the communication link is queried, and a communication token between the sending end and the receiving end is created according to the link attribute and the port mapping relationship.

3. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The analyzing, according to the interaction data, the request purpose corresponding to the data interaction request includes: Extracting a label corresponding to each data in the interactive data to obtain a data label, and calculating a label weight corresponding to the data label; According to the tag weight, the data tag is subjected to tag filtering to obtain a target data tag, and the text information corresponding to the target data tag and the data interaction request is extracted respectively to obtain a tag text and a request text; Semantic parsing is performed on the label text and the request text respectively to obtain label semantics and request semantics, and the request purpose corresponding to the data interaction request is analyzed according to the label semantics and the request semantics.

4. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The determining, from the module data, the to-be-interacted data corresponding to the data interaction request in combination with the request purpose and the interaction data, includes: Performing attribute parsing on the request purpose and the interaction data respectively to obtain a first attribute and a second attribute, and constructing matrices corresponding to the first attribute and the second attribute respectively to obtain a first matrix and a second matrix; Performing matrix fusion on the first matrix and the second matrix to obtain a fusion matrix, performing attribute analysis on each data in the to-be-interacted data to obtain a third attribute, and constructing a matrix corresponding to the third attribute to obtain a third matrix; Calculate the matrix similarity between the fusion matrix and the third matrix, and when the matrix similarity is greater than a preset similarity, determine the to-be-interacted data corresponding to the data interaction request from the module data.

5. The multi-terminal data interaction method based on rail transit as claimed in claim 4, characterized in that: The calculating the matrix similarity between the fusion matrix and the third matrix includes: The matrix similarity between the fusion matrix and the third matrix is ​​calculated by the following formula: Among them, F represents the matrix similarity between the fusion matrix and the third matrix, E represents the matrix set of the fusion matrix and the third matrix, e represents the value of the matrix set, Gi,e represents the ratio of the fusion matrix in the matrix set, represents the average ratio of the fusion matrix in the matrix set, Gi +1 ,e represents the ratio of the third matrix in the matrix set, Represents the average ratio of the third matrix in the matrix set.

6. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The step of selecting data associated with the to-be-interacted data from the module data according to the data support includes: According to the data support, the number of times each data in the module data supports the data to be interacted with is measured; Calculating the confidence of each data and the data to be interacted with according to the number of supports to obtain data confidence; Performing a weighted summation on the data confidence and the data support to obtain a correlation coefficient between each data and the data to be interacted; According to the correlation coefficient, data associated with the data to be interacted with is screened out from the module data.

7. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The performing data normalization processing on the data to be interacted with to obtain normalized data includes: Performing linear processing on the data to be interacted with to obtain data linear values ​​corresponding to the data to be interacted with, and performing scaling processing on each data in the data to be interacted with according to the data linear values ​​to obtain scaled interaction data; Querying the interaction rules corresponding to each module in the interaction module, assigning weights to the interaction rules to obtain rule weight values, and integrating the interaction rules according to the rule weight values ​​to obtain target interaction rules; According to the target interaction rule, the scaled interaction data is normalized using a preset normalization method to obtain normalized data.

8. The multi-terminal data interaction method based on rail transit as claimed in claim 1, characterized in that: The step of scaling each data in the to-be-interacted data according to the data linear value to obtain scaled interactive data includes: Each data in the to-be-interacted data is scaled using the following formula: Among them, xnew represents the scaled interactive data obtained after scaling, x represents the original data corresponding to the interactive data, xmax represents the linear maximum value in the linear value of the data, and xmin represents the linear minimum value in the linear value of the data.

9. The multi-terminal data interaction system based on rail transit is characterized by: The system comprises: A management platform acquisition module is used to acquire a data management platform applied to rail transit management, wherein the data management platform includes: a sending end and a receiving end; A communication token building module, used for creating a communication token between the sending end and the receiving end before receiving the data interaction request sent by the sending end, and transmitting the data interaction request in the sending end to the receiving end based on the communication token; an interaction data determination module, configured to identify a service module in the receiving end, extract module data in each module in the service module, obtain interaction data corresponding to the data interaction request, analyze a request purpose corresponding to the data interaction request according to the interaction data, and determine to-be-interacted data corresponding to the data interaction request from the module data in combination with the request purpose and the interaction data; A support calculation module, used to calculate the support between the module data and the data to be interacted with by the following formula to obtain data support; Among them, H represents the data support between the module data and the data to be interacted, K represents the total number of module data and the data to be interacted, n represents the sequence number of the module data and the data to be interacted, Mn represents the basic probability value corresponding to the nth module data, Pn +1 represents the basic probability value corresponding to the n+1th data to be interacted, and L represents the data length of the nth module data and the n+1th data to be interacted; A related data screening module, used for screening data related to the to-be-interacted data from the module data according to the data support, to obtain related interaction data; A data interaction module is used to query the module corresponding to the data to be interacted in the service module to obtain an interaction module, calculate the interaction conflict coefficient between the interaction modules, and if the interaction conflict coefficient is greater than a preset conflict coefficient, perform data normalization processing on the data to be interacted to obtain normalized data, perform data integration on the associated interaction data and the normalized data to obtain integrated interaction data, and execute data interaction corresponding to the interaction request through the integrated interaction data, wherein the calculation of the interaction conflict coefficient between the interaction modules includes: The interaction conflict coefficient between the interaction modules is calculated by the following formula: Where Q(j, j+1) represents the interaction conflict coefficient between the jth interaction module and the j+1th interaction module, j and j+1 represent the sequence numbers of the interaction modules, z represents the total number of interaction modules, Rj represents the number of conflicts corresponding to the jth interaction module, Tj +1 It represents the number of conflicts corresponding to the j+1th interaction module, and ∏ represents the product operator symbol.

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