Multi-source data feedback collaboration method and system based on data center

Abstract

The invention discloses a multi-source data feedback collaboration method and system based on a data center, and belongs to the technical field of multi-platform collaboration. The method comprises the following steps of: establishing a multi-source feedback state matrix to capture response behavior characteristics under feedback time nodes and track the consistency of inspection response state codes by cooperating a data service instruction of a data middle platform with an external platform and utilizing a response state of an interface end feedback data service, and if the response state codes are found to be inconsistent, judging whether the response state codes are inconsistent or not; and if not, analyzing the feedback delay degree of the data middle platform through iterative tracking, and generating an early warning signal and broadcasting the early warning signal to each external platform. According to the method, full-process automation of data collaboration, response behavior capture and quantification, tracking inspection, consistency analysis and early warning processing is realized, the response capability for multi-party services can be improved in a cross-platform manner, data requirements and service requests from a third party can be responded more quickly and more accurately, and the service quality of the third party is improved. And the collaborative efficiency, accuracy and consistency of multi-source data feedback are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-platform collaboration, and specifically to a multi-source data feedback collaboration method and system based on a data center. Background Art

[0002] With the rapid development of information technology, the sources of enterprise data are becoming increasingly diverse, including internal systems, external partners, third-party service providers, etc. This multi-source data environment provides enterprises with rich information resources, but at the same time also brings challenges in data management and integration; in order to effectively manage and utilize these multi-source data, the concept of a data center has emerged. As an enterprise-level data asset management platform, the data center can centrally store, process, and analyze data from different channels, providing strong support for business decisions.

[0003] In a multi-source data environment, the data center needs to collaborate with external platforms for data to ensure the accuracy and timeliness of the data; however, in actual operation, due to reasons such as network latency, system errors, or data inconsistencies, problems may occur in the data feedback between the data center and external platforms. These problems not only affect the accuracy of the data but may also lead to mistakes in business decisions.

[0004] Currently, although there are some data collaboration methods in the market, most of them are for single data sources or specific scenarios and are difficult to adapt to the complex requirements in a multi-source data environment. These methods usually lack a unified data feedback mechanism and cannot effectively capture and respond to the feedback status of external platforms; in addition, when dealing with data inconsistencies, existing data collaboration methods often adopt simple retry or warning strategies and lack in-depth analysis and warning mechanisms for data center feedback latency. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-source data feedback collaboration method and system based on a data center to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] A multi-source data feedback collaboration system based on a data center, the system includes: a data service module, a response behavior log module, a tracking and inspection module, and an early warning and processing module;

[0008] The data service module generates data service instructions in collaboration with external platforms through the data center and feeds back the response status of the data service through an interface end;

[0009] The response behavior log module, by triggering the protocol mechanism, enables the interface side to feedback the response status code, and captures the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix;

[0010] The tracking and inspection module is used to quantify the multi-source feedback status matrix at different feedback time nodes by using 0 and 1 as response conditions when capturing the response behavior characteristics, and continuously track and inspect the consistency of the response status code between each external platform through the interface side;

[0011] The early warning processing module is used to lock the tracking representation matrix with inconsistent response status codes, analyze and calculate the feedback delay degree of the data middle platform through iterative tracking, so as to generate an early warning signal and broadcast it to each external platform.

[0012] Further, the data service module includes an instruction configuration unit and a response status cache unit;

[0013] The instruction configuration unit configures data service instructions based on the protocol mechanism of the data service. The protocol mechanism is used to implement different data service functions. The data service instructions trigger the protocol mechanism through the interface side and implement the response of the data service;

[0014] The response status cache unit is used to allocate data storage space. The data storage space is integrated in the data platform and is used to cache the response status of the data service of the external platform. And the data storage space is internally connected to the interface side configured in the data middle platform. The interface side is connected to the external platform and is used to realize data collaboration between the external platforms.

[0015] Further, the response behavior log module includes an encoding unit and a response behavior capture unit;

[0016] The encoding unit uniformly encodes the interface side, the response status of the data service, and the external platform through the data middle platform. Among them, one interface side corresponds to triggering one protocol mechanism and is used to feedback the response status of one data service, and one response status of the data service has a response status code with a unique encoding attribute;

[0017] The response behavior capture unit is used to initialize the feedback time node sequence, capture the response status code generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status code; the row number of the multi-source feedback status matrix is the encoding number of the external platform, and the column number of the multi-source feedback status matrix is the encoding number of the interface side.

[0018] Further, the tracking and inspection module includes a condition quantification unit and a consistency analysis unit;

[0019] The conditional quantization unit is used to capture the response status code generated by the external platform fed back at the interface end at the feedback time node, and perform quantization of response conditions 0 and 1 to obtain a multi-source feedback status matrix at the feedback time node;

[0020] The consistency analysis unit is used to construct a tracking characterization model, and through the dynamic flow at the feedback time node, characterize the tracking behavior of the interface end among external platforms, so as to judge the consistency of the response status codes fed back by the data middle platform among external platforms.

[0021] Further, the early warning processing module includes an iterative tracking unit and an early warning broadcast unit;

[0022] The iterative tracking unit is used to lock the tracking characterization matrix with inconsistent response status codes, and analyze and calculate the feedback delay degree of the data middle platform by means of iterative tracking;

[0023] The early warning broadcast unit is used to preset a feedback delay degree threshold. If the feedback delay degree is greater than or equal to the feedback delay degree threshold, the data middle platform generates an early warning signal and broadcasts it to each external platform.

[0024] A multi-source data feedback collaboration method based on a data middle platform, this method includes the following steps:

[0025] Step S1: Collaborate with the external platform through the data middle platform to generate a data service instruction, and feedback the response status of the data service through the interface end;

[0026] Step S2: When the interface end feeds back the response status code by triggering the protocol mechanism, capture the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix;

[0027] Step S3: When capturing the response behavior characteristics, use 0 and 1 as response conditions to quantify the multi-source feedback status matrix at different feedback time nodes, and continuously track and inspect the consistency of the response status codes of the interface end among external platforms;

[0028] Step S4: Lock the tracking characterization matrix with inconsistent response status codes, analyze and calculate the feedback delay degree of the data middle platform by means of iterative tracking, so as to generate an early warning signal and broadcast it to each external platform.

[0029] Further, the specific implementation process of step S1 includes:

[0030] Configure the data service instruction based on the protocol mechanism of the data service. The protocol mechanism is used to implement different data service functions. The data service instruction triggers the protocol mechanism through the interface end and realizes the response of the data service;

[0031] The interface end is configured in the data middle platform, and the interface end is connected to an external platform and used to realize data collaboration between external platforms. The interface end is also internally connected to a data storage space, and the data storage space is integrated in the data platform and used to cache the response status of the data service of the external platform.

[0032] Further, the specific implementation process of step S2 includes:

[0033] The interface end, the response status of the data service, and the external platform are respectively encoded uniformly through the data middle platform. Among them, one interface end corresponds to triggering a protocol mechanism and is used to feedback the response status of a data service, and a response status of a data service has a response status code with a unique encoding attribute;

[0034] Initialize the feedback time node sequence, capture the response status codes generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status codes; the row number of the multi-source feedback status matrix is the encoding number of the external platform, and the column number of the multi-source feedback status matrix is the encoding number of the interface end.

[0035] Further, the specific implementation process of step S3 includes:

[0036] Denote any i-th external platform as E i , denote any j-th interface end as P j , denote the response status code feedback by the interface end P j as C j ; at the x-th feedback time node t x , if the response status code C j generated by the external platform E i feedback by the interface end P j is captured, then let the matrix position r ij at the i-th row and j-th column of the multi-source feedback status matrix be 1. If the response status code C j generated by the external platform E i feedback by the interface end P j is not captured, then let the matrix position r ij at the i-th row and j-th column of the multi-source feedback status matrix be 0, and obtain the multi-source feedback status matrix R(t x ) at the feedback time node t x ;

[0037] Through the dynamic transfer of the feedback time node, characterize the tracking behavior of the interface end between external platforms, and construct a tracking characterization model: the dynamically transferred tracking characterization matrix RR(t x ) = R(t x ) ∩ R0, where R0 is a matrix of all 1s;

[0038] If RR(t x ) = R0, it indicates that the response status codes fed back between the data center and each external platform are consistent. If RR(t x ) ≠ R0, it indicates that the response status codes fed back between the data center and each external platform are inconsistent.

[0039] Furthermore, the specific implementation process of step S4 includes:

[0040] Lock the tracking representation matrix RR(t x ) with inconsistent response status codes, and analyze and calculate the feedback delay of the data center through iterative tracking:

[0041] Let x = x + 1, return to the tracking representation model, and obtain the dynamically flowing tracking representation matrix RR(t x+1 ). If RR(t x+1 ) = R0, the iterative tracking stops. If RR(t x+1 ) ≠ R0, continue the iterative tracking;

[0042] When the iterative tracking stops, obtain the y-th feedback time node t y at the end of the iterative tracking, and y ≠ x;

[0043] Calculate the feedback delay of the data center In the formula, Δt represents the fixed interval duration between adjacent feedback time nodes, and NUM[RR(t x )] represents the number of 0s in the tracking representation matrix RR(t x );

[0044] Preset a feedback delay threshold. If the feedback delay is greater than or equal to the feedback delay threshold, the data center generates a warning signal and broadcasts it to each external platform;

[0045] In the above method, the data center needs to keep the data services of each external platform consistent, and the response status code is a concrete representation of consistency. At the current feedback time node, the data service conditions of each external platform are generated and captured. The value 1 represents the representation condition of consistent data services. The all-1 matrix is a judgment matrix used to judge the response consistency. When there is a 0, it indicates that the response is inconsistent. The denominator of the feedback delay calculation formula is the number of inconsistencies, and the numerator is the duration of the feedback delay. The greater the feedback delay, the lower the efficiency of data consistency coordination.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In a multi-source data feedback collaboration method and system based on a data middle platform provided by the present invention, data service instructions of an external platform are collaborated through the data middle platform, the response status of the data service is fed back by an interface end, a multi-source feedback status matrix is constructed to capture the response behavior characteristics at the feedback time node, the consistency of the inspection response status code is tracked. If it is found that the response status codes are inconsistent, the feedback latency of the data middle platform is analyzed through iterative tracking, and a warning signal is broadcast to each external platform. The present invention realizes the full-process automation of data collaboration, response behavior capture and quantification, tracking inspection and consistency analysis, and warning processing, can cross platforms to improve the response ability for multi-party services, can respond more quickly and accurately to data requirements and service requests from third parties, and effectively improves the collaboration efficiency, accuracy, and consistency of multi-source data feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0048] Figure 1 It is a schematic diagram of the steps of a multi-source data feedback collaboration method based on a data middle platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] In the first embodiment: A multi-source data feedback collaboration system based on a data middle platform is provided. The system includes: a data service module, a response behavior log module, a tracking inspection module, and a warning processing module;

[0051] The data service module collaborates data service instructions generated by an external platform through the data middle platform, and feeds back the response status of the data service through an interface end;

[0052] Among them, the data service module includes an instruction configuration unit and a response status cache unit;

[0053] The instruction configuration unit configures data service instructions based on the protocol mechanism of the data service. The protocol mechanism is used to implement different data service functions. The data service instructions trigger the protocol mechanism through the interface end and realize the response of the data service;

[0054] The response status cache unit is used to allocate data storage space, which is integrated in the data platform and used to cache the response status of data services from external platforms. The data storage space is internally connected to the interface end configured in the data middle platform, and the interface end is connected to the external platform and used to achieve data collaboration between external platforms.

[0055] The response behavior log module, by triggering the protocol mechanism, enables the interface end to feedback the response status code, and captures the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix.

[0056] Among them, the response behavior log module includes an encoding unit and a response behavior capture unit.

[0057] The encoding unit uniformly encodes the interface end, the response status of the data service, and the external platform through the data middle platform. Among them, one interface end corresponds to triggering one protocol mechanism and is used to feedback the response status of one data service, and one response status of the data service has a response status code with a unique encoding attribute.

[0058] The response behavior capture unit is used to initialize the feedback time node sequence, capture the response status code generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status code; the row number of the multi-source feedback status matrix is the encoding number of the external platform, and the column number of the multi-source feedback status matrix is the encoding number of the interface end.

[0059] The tracking and inspection module is used to quantify the multi-source feedback status matrix at different feedback time nodes by using 0 and 1 as response conditions when capturing response behavior characteristics, and continuously track and inspect the consistency of the response status code between each external platform through the interface end.

[0060] Among them, the tracking and inspection module includes a condition quantification unit and a consistency analysis unit.

[0061] The condition quantification unit is used to capture the response status code generated by the external platform feedback by the interface end at the feedback time node, and perform quantification of response conditions 0 and 1 to obtain the multi-source feedback status matrix at the feedback time node.

[0062] The consistency analysis unit is used to construct a tracking characterization model, and characterize the tracking behavior of the interface end between each external platform through the dynamic transfer of the feedback time node, so as to judge the consistency of the response status code feedback by the data middle platform between each external platform.

[0063] The early warning processing module is used to lock the tracking characterization matrix with inconsistent response status codes, and through iterative tracking, analyze and calculate the feedback latency of the data middle platform to generate an early warning signal and broadcast it to each external platform;

[0064] Among them, the early warning processing module includes an iterative tracking unit and an early warning broadcasting unit;

[0065] The iterative tracking unit is used to lock the tracking characterization matrix with inconsistent response status codes, and through iterative tracking, analyze and calculate the feedback latency of the data middle platform;

[0066] The early warning broadcasting unit is used to preset a feedback latency threshold. If the feedback latency is greater than or equal to the feedback latency threshold, the data middle platform generates an early warning signal and broadcasts it to each external platform.

[0067] Please refer to Figure 1 , in the second embodiment: A multi-source data feedback collaboration method based on the data middle platform is provided for application in the first embodiment above. The method includes the following steps:

[0068] Step S1: Collaborate with the data service instructions generated by the external platform through the data middle platform, and feedback the response status of the data service through the interface end;

[0069] Exemplarily, based on the protocol mechanism of the data service, configure the data service instructions. The protocol mechanism is used to implement different data service functions. The data service instructions trigger the protocol mechanism through the interface end and implement the response of the data service;

[0070] The interface end is configured in the data middle platform, and the interface end is connected to the external platform and used to realize data collaboration between external platforms. The interface end is also internally connected to the data storage space. The data storage space is integrated in the data platform and used to cache the response status of the data service of the external platform.

[0071] Step S2: When the interface end feedbacks the response status code by triggering the protocol mechanism, capture the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix;

[0072] Exemplarily, the interface end, the response status of the data service, and the external platform are uniformly encoded through the data middle platform. Among them, one interface end corresponds to triggering one protocol mechanism and is used to feedback the response status of one data service, and the response status of one data service has a response status code with a unique encoding attribute;

[0073] Initialize the feedback time node sequence, capture the response status codes generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status codes; the row number of the multi-source feedback status matrix is the coding number of the external platform, and the column number of the multi-source feedback status matrix is the coding number of the interface end.

[0074] Step S3: When capturing the response behavior characteristics, use 0 and 1 as response conditions to quantify the multi-source feedback status matrix under different feedback time nodes, and continuously track and inspect the consistency of the response status codes among external platforms through the interface end;

[0075] Exemplarily, denote any i-th external platform as E i , denote any j-th interface end as P j , denote the response status code feedback by the interface end P j as C j ; at the x-th feedback time node t x , if the response status code C j generated by the external platform E i feedback by the captured interface end P j is obtained, then let the matrix position r ij at the i-th row and j-th column of the multi-source feedback status matrix be 1. If the response status code C j generated by the external platform E i feedback by the interface end P j is not captured, then let the matrix position r ij at the i-th row and j-th column of the multi-source feedback status matrix be 0, and obtain the multi-source feedback status matrix R(t x ) at the feedback time node t x ;

[0076] Characterize the tracking behavior of the interface end among external platforms through the dynamic transfer of feedback time nodes, and construct a tracking characterization model: the dynamically transferred tracking characterization matrix RR(t x ) = R(t x ) ∩ R0, where R0 is a matrix of all 1s;

[0077] If RR(t x ) = R0, it means that the response status codes fed back by the data middle platform among external platforms are consistent. If RR(t x ) ≠ R0, it means that the response status codes fed back by the data middle platform among external platforms are not consistent.

[0078] Step S4: Lock the tracking characterization matrix with inconsistent response status codes, and analyze and calculate the feedback delay degree of the data middle platform through iterative tracking to generate a warning signal and broadcast it to each external platform;

[0079] Exemplarily, the locked response status code does not have a consistent tracking representation matrix RR(t x ), and by means of iterative tracking, analyze and calculate the feedback latency of the data middle platform:

[0080] Let x = x + 1, return the tracking representation model, and obtain the dynamically flowing tracking representation matrix RR(t x+1 ), if RR(t x+1 ) = R0, then the iterative tracking stops, if RR(t x+1 ) ≠ R0, then continue the iterative tracking;

[0081] When the iterative tracking stops, obtain the y-th feedback time node t at the time when the iterative tracking stops y , and y ≠ x;

[0082] Calculate the feedback latency of the data middle platform In the formula, Δt represents the fixed interval duration between adjacent feedback time nodes, and NUM[RR(t x )] represents the number of numerical 0s included in the tracking representation matrix RR(t x );

[0083] Preset a feedback latency threshold. If the feedback latency is greater than or equal to the feedback latency threshold, the data middle platform generates a warning signal and broadcasts it to each external platform.

[0084] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0085] Finally, it should be noted that: the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-source data feedback collaboration method based on a data middle platform, characterized in that The method includes the following steps: Step S1: Collaborate with the external platform through the data middle platform to generate data service instructions, and feedback the response status of the data service through the interface end; Step S2: When the interface end feedbacks the response status code by triggering the protocol mechanism, capture the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix; Step S3: When capturing the response behavior characteristics, use 0 and 1 as response conditions to quantify the multi-source feedback status matrix at different feedback time nodes, and continuously track and inspect the consistency of the response status code among each external platform through the interface end; Step S4: Lock the tracking characterization matrix with inconsistent response status codes, and analyze and calculate the feedback latency of the data middle platform by means of iterative tracking to generate a warning signal and broadcast it to each external platform.

2. The multi-source data feedback and collaboration method based on a data middle platform according to claim 1, wherein The specific implementation process of the said Step S1 includes: Configure data service instructions based on the protocol mechanism of the data service. The protocol mechanism is used to implement different data service functions. The data service instructions trigger the protocol mechanism through the interface end and realize the response of the data service; The interface end is configured in the data middle platform, and the interface end is connected to the external platform and used to realize data collaboration among external platforms. The interface end is also internally connected to a data storage space, and the data storage space is integrated in the data platform and used to cache the response status of the data service of the external platform.

3. A multi-source data feedback collaboration method based on a data middle platform according to claim 1, characterized in that The specific implementation process of the said Step S2 includes: Uniformly encode the interface end, the response status of the data service, and the external platform through the data middle platform. Among them, one interface end corresponds to triggering one protocol mechanism and is used to feedback the response status of one data service, and the response status of one data service has a response status code with a unique encoding attribute; Initialize the feedback time node sequence, capture the response status code generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status code; the row number of the multi-source feedback status matrix is the encoding number of the external platform, and the column number of the multi-source feedback status matrix is the encoding number of the interface end.

4. A multi-source data feedback collaboration method based on a data middle platform according to claim 1, characterized in that The specific implementation process of the said Step S3 includes: Denote any \(i\)-th external platform as \(E\). i Denote any \(j\)-th interface end as \(P\). j Denote the response status code fed back by the interface end \(P\). j as \(C\). j At the \(x\)-th feedback time node \(t\). x If the response status code \(C\). j fed back by the external platform \(E\). i generated by the interface end \(P\) is captured, j then set the matrix position \(r\). ij at the \(i\)-th row and \(j\)-th column of the multi-source feedback status matrix to 1. If the response status code \(C\). j fed back by the external platform \(E\). i generated by the interface end \(P\) is not captured, j then set the matrix position \(r\). ij at the \(i\)-th row and \(j\)-th column of the multi-source feedback status matrix to 0, and obtain the multi-source feedback status matrix \(R(t\). x ) at the feedback time node \(t\). x ) Characterize the tracking behavior of the interface end among external platforms through the dynamic flow of feedback time nodes, and construct a tracking characterization model: the tracking characterization matrix RR(t x ) = R(t x ) ∩ R0, where R0 is a matrix of all 1s; If RR(t x ) = R0, it means that the response status codes fed back by the data middle platform among various external platforms are consistent. If RR(t x ) ≠ R0, it means that the response status codes fed back by the data middle platform among various external platforms are not consistent.

5. A multi-source data feedback and collaboration method based on a data middle platform according to claim 4, characterized in that The specific implementation process of the said Step S4 includes: The tracking representation matrix RR(t of the locked response status code lacks consistency x ), and through the iterative tracking method, analyze and calculate the feedback latency of the data middle platform: Let \(x = x + 1\), return the tracking representation model, and obtain the dynamically evolving tracking representation matrix \(RR(t x+1 )\). If \(RR(t x+1 ) = R0\), the iterative tracking stops. If \(RR(t x+1 )\neq R0\), continue the iterative tracking; When the iterative tracking stops, obtain the y-th feedback time node t at which the iterative tracking stops y , and y ≠ x; Calculate the feedback latency of the data computing middleware where Δt represents the fixed interval duration between adjacent feedback time nodes, and NUM[RR(t x )] represents the number of numerical 0s contained in the tracking characterization matrix RR(t x ); Preset a feedback latency threshold. If the feedback latency is greater than or equal to the feedback latency threshold, the data middle platform generates a warning signal and broadcasts it to each external platform.

6. A multi-source data feedback collaboration system based on a data middle platform, which executes a multi-source data feedback collaboration method based on a data middle platform as described in any one of claims 1-5, characterized in that The system includes: a data service module, a response behavior log module, a tracking and inspection module, and a warning processing module; The data service module collaborates with the external platform through the data middle platform to generate data service instructions, and feedbacks the response status of the data service through the interface end; The response behavior log module, by triggering the protocol mechanism, enables the interface end to feedback the response status code, and captures the response behavior characteristics of each external platform at the feedback time node through the multi-source feedback status matrix; The tracking and inspection module is used to, when capturing the response behavior characteristics, use 0 and 1 as response conditions to quantify the multi-source feedback status matrix at different feedback time nodes, and continuously track and inspect the consistency of the response status code among each external platform through the interface end; The early warning processing module is used to lock the tracking characterization matrix with inconsistent response status codes, and analyze and calculate the feedback latency of the data middle platform by means of iterative tracking, so as to generate early warning signals and broadcast them to each external platform.

7. A multi-source data feedback and collaboration system based on a data middle platform according to claim 6, characterized in that, The data service module includes an instruction configuration unit and a response status cache unit; The instruction configuration unit configures data service instructions based on the protocol mechanism of data services. The protocol mechanism is used to implement different data service functions. The data service instructions trigger the protocol mechanism through the interface end and implement the response of data services; The response status cache unit is used to allocate data storage space. The data storage space is integrated in the data platform and is used to cache the response status of data services of external platforms. And the data storage space is internally connected to the interface end configured in the data middle platform. The interface end is connected to the external platform and is used to realize data collaboration between external platforms.

8. A multi-source data feedback collaboration system based on a data middle platform according to claim 6, characterized in that, The response behavior log module includes an encoding unit and a response behavior capture unit; The encoding unit uniformly encodes the interface end, the response status of data services, and the external platform through the data middle platform. Among them, one interface end corresponds to triggering one protocol mechanism and is used to feedback the response status of one data service. And the response status of one data service has a response status code with a unique encoding attribute; The response behavior capture unit is used to initialize the feedback time node sequence, capture the response status codes generated when each protocol mechanism is triggered at each feedback time node, and construct a multi-source feedback status matrix to record the captured response status codes; The row number of the multi-source feedback status matrix is the encoding number of the external platform, and the column number of the multi-source feedback status matrix is the encoding number of the interface end.

9. The multi-source data feedback and collaboration system based on a data middle platform according to claim 6, characterized in that, The tracking and inspection module includes a condition quantification unit and a consistency analysis unit; The condition quantification unit is used to capture the response status codes generated by the external platform feedback by the interface end at the feedback time node, and perform quantification of response conditions 0 and 1 to obtain the multi-source feedback status matrix at the feedback time node; The consistency analysis unit is used to construct a tracking characterization model, and characterize the tracking behavior of the interface end between each external platform through the dynamic flow of the feedback time node, so as to judge the consistency of the response status codes feedback by the data middle platform between each external platform.

10. A multi-source data feedback and collaboration system based on a data middle platform according to claim 6, characterized in that, The early warning processing module includes an iterative tracking unit and an early warning broadcast unit; The iterative tracking unit is used to lock the tracking characterization matrix with inconsistent response status codes, and analyze and calculate the feedback latency of the data middle platform by means of iterative tracking; The early warning broadcast unit is used to preset a feedback latency threshold. If the feedback latency is greater than or equal to the feedback latency threshold, the data middle platform generates an early warning signal and broadcasts it to each external platform.

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