A method and system for automatic monitoring of a server test environment

Abstract

The present application relates to the technical field of distributed system testing and operation and maintenance monitoring, and particularly relates to a server test environment automatic monitoring method and system, comprising: collecting a micro-service cluster directed call topology structure, constructing a distributed cell layer with node and edge vector spaces; parsing data serialization rules to extract constraint restriction mapping matrices, assembling a Hodge-Dirac operator; obtaining real-time business state vector groups to splice into zero-order co-chains, using the operator to solve an algebraic Laplace matrix to calculate a quadratic form energy functional; when determining that the quadratic form energy functional is greater than zero, generating an early warning signal, constructing and solving a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, obtaining a state compensation update matrix; finally, analyzing the state compensation update matrix into reverse rollback and cache erasure instructions, and submitting the instructions to a service mesh data plane proxy component for execution. The present application realizes code-free cross-node semantic conflict quantification and distributed consistency state self-healing closed loop.

Description

Technical Field

[0001] This invention relates to the field of distributed system testing and operation and maintenance monitoring technology, and in particular to an automatic monitoring method and system for server testing environments. Background Technology

[0002] In high-concurrency stress testing scenarios of large-scale distributed software architectures, the test environment typically deploys an extremely large number of microservice nodes. Traditional automated monitoring solutions for server test environments mostly rely on extracting basic hardware performance indicators such as the liveness status of microservice nodes and CPU load, or analyzing the binary call connectivity between microservice nodes based on distributed tracing logs.

[0003] However, when faced with the impact of extreme concurrent test traffic, microservice clusters often experience data serialization and protocol conversion failures between service nodes due to network jitter or distributed transaction synchronization delays. Traditional monitoring methods, lacking a quantitative measurement mechanism for the consistency of application-layer business logic, cannot penetrate the business layer to accurately identify and intercept hidden distributed state conflicts and implicit dirty data write defects. This makes the test system highly susceptible to deep test data pollution and cascading errors when local semantic split-brain occurs, causing technical problems such as the breakdown of automated testing processes and the high cost of manual troubleshooting and rollback. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an automatic monitoring method and system for server testing environments, aiming to improve the problem that traditional test monitoring mostly relies on hardware indicators and call log tracking, which cannot measure logical discrepancies in cross-node data transformation, thus making it difficult to automatically locate and repair hidden distributed dirty data split-brain issues.

[0005] In a first aspect, the present invention provides the following technical solution: an automatic monitoring method for a server testing environment, comprising the following steps:

[0006] S1. Collect the directed call topology of the microservice cluster in the server test environment and construct a distributed cellular layer with node vector space and edge vector space.

[0007] S2. Parse the data serialization rules of the microservice cluster, extract the constraint mapping matrix that maps the node vector space to the edge vector space, and assemble the Hatch-Dirac operator based on the constraint mapping matrix;

[0008] S3. Obtain the real-time business state vector group of the microservice cluster and concatenate it into a zero-order co-chain. Use the Hatch-Dirac operator to solve the algebraic Laplace matrix and calculate the quadratic energy functional of the zero-order co-chain under the algebraic Laplace matrix.

[0009] S4. When the quadratic energy functional is determined to be greater than zero, an early warning signal is generated. Based on the algebraic Laplace matrix, a discrete layer diffusion dynamics equation is constructed, and the state compensation update matrix is ​​obtained by solving the discrete layer diffusion dynamics equation.

[0010] S5. The state compensation update matrix is ​​parsed into reverse rollback instructions and cache erase instructions, and sent to the service mesh data plane proxy component for execution.

[0011] By adopting the above technical solution, the Hatchdirac operator is constructed to compute functionals and solve dynamic equations to issue instructions, thereby realizing the algebraic quantification and automated self-healing of semantic conflicts in microservices. This improves the problem that traditional test monitoring mostly relies on hardware indicators and call logs for tracking, which cannot measure the logical discrepancies in cross-node data transformation, resulting in the difficulty in automatically locating and repairing hidden distributed dirty data split-brain.

[0012] Optionally, in S1, the directed call topology of the microservice cluster in the data acquisition server test environment, constructing a distributed cellular layer with node vector space and edge vector space includes:

[0013] Intercept concurrent request tracing logs between microservice nodes in the microservice cluster;

[0014] Based on the concurrent request tracing logs, extract the calling microservice node, the called microservice node, and the communication link between them, and construct a finite graph topology base space composed of a set of microservice nodes and a set of communication links;

[0015] Extract the internal business state variable dimension of each microservice node in the microservice node set, and assign the node vector space with matching dimension to the corresponding microservice node;

[0016] Extract the application interface load protocol specification of each communication link in the communication link set, and assign the edge vector space with matching dimensions to the corresponding communication link;

[0017] The distributed cellular layer is generated by combining the finite graph topology base space, the node vector space, and the edge vector space through an algebraic structure mapping.

[0018] Optionally, in S2, parsing the data serialization rules of the microservice cluster and extracting the constraint mapping matrix from the node vector space to the edge vector space includes:

[0019] Intercept the interface definition files between the microservice node and the communication link where the call is associated;

[0020] Lexical analysis is performed on the interface definition file to extract field type dimension constraints and business logic conservation transformation rules from the communication data structure;

[0021] Transform the field type dimension constraints and the business logic conservation conversion rules into an algebraic mapping relationship;

[0022] Based on the algebraic mapping relationship, construct the constraint mapping matrix that determines the linear projection rule from the node vector space to the edge vector space.

[0023] Optionally, in S2, assembling the Hatch-Dirac operator based on the constraint mapping matrix includes:

[0024] The constraint mapping matrix is ​​configured as a common edge operator to indicate the forward differential evolution of the state vector along the communication link;

[0025] Perform a conjugate transpose operation on the common edge operator to obtain an adjoint boundary operator used to indicate the reverse aggregation evolution of the state vector along the communication link;

[0026] Construct an initial block matrix with zero blocks along the main diagonal;

[0027] The adjoint boundary operator is filled into the first off-diagonal block position of the initial block matrix, and the common edge operator is filled into the second off-diagonal block position of the initial block matrix to generate the Hatch-Dirac operator.

[0028] Optionally, in S3, solving the algebraic Laplace matrix using the Hatch-Dirac operator includes:

[0029] Perform matrix multiplication on the Hatch-Dirac operator to obtain the initial result matrix;

[0030] Extract the non-zero block matrix along the main diagonal from the initial operation result matrix;

[0031] The non-zero block matrix along the main diagonal is established as the algebraic Laplace matrix.

[0032] Optionally, in S3, calculating the quadratic energy functional of the zero-order cochain under the algebraic Laplace matrix includes:

[0033] Perform a transpose operation on the zero-order cochain to generate a transposed zero-order cochain;

[0034] The intermediate row vector is obtained by performing the first product operation between the transposed zero-order cochain matrix and the algebraic Laplace matrix.

[0035] The scalar value is obtained by performing a second product operation between the intermediate row vector and the zero-order co-linked vector.

[0036] The scalar value is established as the quadratic energy functional.

[0037] Optionally, in S4, the step of constructing the discrete layer diffusion dynamics equation based on the algebraic Laplace matrix and solving the discrete layer diffusion dynamics equation to obtain the state compensation update matrix includes:

[0038] Using the geometric constraint of forcing the state sequence of the zero-order co-chain to project onto the zero-order cohomology group space, the discrete layer diffusion dynamics equation is constructed with the time variable as the independent variable and the negative matrix of the algebraic Laplace matrix as the coefficient matrix.

[0039] Perform eigenvalue decomposition on the algebraic Laplace matrix;

[0040] Extract the eigenvector corresponding to the smallest non-zero eigenvalue from the output of the eigenvalue decomposition operation;

[0041] Using the eigenvector as the steepest descent search direction, the discrete layer diffusion dynamics equation is solved by differential iteration, and the directional components generated by the iterative convergence are extracted to form the state compensation update matrix.

[0042] Optionally, in S5, parsing the state compensation update matrix into reverse rollback instructions and cache erase instructions includes:

[0043] Traverse each element of the state compensation update matrix to locate non-zero matrix elements;

[0044] Map the non-zero matrix elements to the target microservice node and the target communication buffer where a state conflict occurs;

[0045] Extract the magnitude and sign characteristics of the non-zero matrix elements;

[0046] The amplitude and symbol characteristics are translated into a reverse rollback instruction that instructs the target microservice node to restore historical checkpoint business data.

[0047] The amplitude and symbol characteristics are translated into a cache erase instruction that instructs the target communication cache to perform a dirty data destruction operation.

[0048] Optionally, in S5, the execution of sending the data to the service mesh data plane proxy component includes:

[0049] Establish a bypass injection communication channel for the service mesh data plane proxy component deployed at the bottom layer of the microservice cluster;

[0050] The reverse rollback instruction and the cache erase instruction are pushed to the policy execution module of the service mesh data plane proxy component through the bypass injection communication channel;

[0051] The policy execution module is triggered to intercept application layer network communication traffic across microservice nodes, and in the intercepted state, the local business status register is overwritten based on the reverse rollback instruction and the cache erase instruction.

[0052] Secondly, the present invention provides the following technical solution: an automatic monitoring system for a server testing environment, comprising the following modules:

[0053] The topology base construction module is used to collect the directed call topology of the microservice cluster in the server test environment and construct a distributed cell layer with node vector space and edge vector space.

[0054] The operator mapping configuration module is used to parse the data serialization rules of the microservice cluster, extract the constraint mapping matrix that maps the node vector space to the edge vector space, and assemble the Hatch-Dirac operator based on the constraint mapping matrix.

[0055] The energy functional monitoring module is used to obtain the real-time business status vector group of the microservice cluster and concatenate it into a zero-order co-chain. It uses the Hatch-Dirac operator to solve the algebraic Laplace matrix and calculates the quadratic energy functional of the zero-order co-chain under the algebraic Laplace matrix.

[0056] The diffusion equation solving module is used to generate an early warning signal when the quadratic energy functional is determined to be greater than zero, construct a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, and solve the discrete layer diffusion dynamics equation to obtain the state compensation update matrix.

[0057] The closed-loop compensation execution module is used to parse the state compensation update matrix into reverse rollback instructions and cache erase instructions, and send them to the service mesh data plane proxy component for execution.

[0058] The present invention has the following beneficial effects:

[0059] 1. In this invention, by constructing the Hatch-Dirac operator to calculate the functional and solve the dynamic equation to issue instructions, the algebraic quantification and automated self-healing of semantic conflicts in microservices are realized. This improves the problem that traditional test monitoring mostly relies on hardware indicators and call log tracking, which cannot measure the logical divergence of cross-node data transformation, thus making it difficult to automatically locate and repair the hidden distributed dirty data split brain.

[0060] 2. In this invention, a distributed cell layer is generated by extracting the business state dimension and allocating the vector space according to the protocol specification. Then, the network link and the upper-layer business data are fused and mapped, thereby improving the problem that traditional topology monitoring mostly uses connectivity detection, which makes it difficult to track the data flow of the application layer due to the separation of physical structure and business logic.

[0061] 3. In this invention, the Laplace matrix is ​​extracted by self-multiplication of the Hatchdirac operator and the quadratic functional scalar is calculated, thereby compressing the massive high-dimensional states into a single criterion. This improves the problem that traditional consistency checks mostly use full log comparison, which causes extremely high computational overhead and is prone to monitoring delays due to the large amount of data under high concurrency.

[0062] 4. In this invention, by parsing the compensation matrix into an instruction bypass injection data plane proxy interception and overwriting the register, the underlying business state repair without code intrusion is achieved. This improves the problem that traditional fault recovery mostly adopts application layer retry logic, which is prone to secondary errors due to excessive reliance on business side intervention. Attached Figure Description

[0063] Figure 1 This is a flowchart of an automatic monitoring method for a server testing environment proposed in this invention;

[0064] Figure 2 This invention presents a flowchart of the state compensation update matrix analysis and closed-loop execution process for an automatic monitoring method for a server testing environment.

[0065] Figure 3 This is an architecture diagram of an automatic monitoring system for server testing environments proposed in this invention. Detailed Implementation

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1:

[0068] In a first embodiment of the present invention, the present invention provides an automatic monitoring method for a server testing environment, such as... Figure 1 As shown, it includes the following steps:

[0069] S1. Collect the directed call topology of the microservice cluster in the server test environment and construct a distributed cellular layer with node vector space and edge vector space.

[0070] Furthermore, in S1, the directed call topology of the microservice cluster in the data acquisition server test environment is collected, and a distributed cellular layer with node vector space and edge vector space is constructed, including:

[0071] Intercept concurrent request tracing logs between microservice nodes in a microservice cluster;

[0072] Based on the concurrent request tracing logs, extract the calling microservice node, the called microservice node, and the communication link between them, and construct a finite graph topology base space composed of the set of microservice nodes and the set of communication links;

[0073] Extract the internal business state variable dimension of each microservice node in the microservice node set, and assign a node vector space with matching dimension to the corresponding microservice node;

[0074] Extract the application interface load protocol specification of each communication link in the communication link set, and assign a matching dimension edge vector space to the corresponding communication link;

[0075] By combining the finite graph topology base space, node vector space, and edge vector space through algebraic structure mapping, a distributed cellular layer is generated.

[0076] Specifically, the input end receives concurrent request tracing logs, internal business state variable dimensions, and application programming interface load balancing protocol specifications. After topology extraction and vector space allocation operations, the output end outputs a distributed cellular layer.

[0077] Construct a finite graph topology base space using intercepted concurrent request tracing logs as the data source. Finite graph topological base space Includes a collection of microservice nodes and communication link set .

[0078] ;

[0079] in This represents the set of microservice nodes contained in the finite graph topology base space. The union operator is represented. It represents the set of communication links contained in the base space of a finite graph topology.

[0080] The system extracts the set of microservice nodes. The internal business state variable dimension of each microservice node. Let a specific microservice node... Belongs to the collection of microservice nodes Specific microservice nodes The internal business state variable dimension is denoted as For a specific microservice node Allocated node vector space Formalization 3D real space:

[0081] ;

[0082] in Indicates isomorphic mapping relationship, Represents the set of real numbers. express A dimensional real vector space.

[0083] Extracting the communication link set The application programming interface (API) payload protocol specification for each communication link. Let's assume a specific communication link... Belongs to the communication link set Specific communication links The feature dimension corresponding to the protocol specification is denoted as For a specific communication link Allocated edge vector space Formalization 3D real space:

[0084] ;

[0085] in express A dimensional real vector space.

[0086] Conventional microservice monitoring methods can only obtain flat hardware indicators such as node liveness or network connectivity. Conventional binary call graphs cannot penetrate the business logic layer to capture dimensional misalignments and semantic conflicts during data serialization. This approach involves extracting concurrent request tracing logs to reconstruct the physical call skeleton, abstracting the business state within microservice nodes into a node vector space, and abstracting the communication link protocol specifications into an edge vector space. A distributed cavity layer is established, stitching together the previously fragmented network communication links and business data flows under the same algebraic topology. Data interaction between cross-microservice nodes is rigorously formalized as mathematical transformations between algebraic vector spaces. Based on this distributed cavity layer, the automatic monitoring system possesses an analytical foundation for quantifying the semantic consistency of cross-node application programming interface data transformations, overcoming the technical limitations of relying solely on error logs to locate distributed transaction split-brain problems.

[0087] S2. Analyze the data serialization rules of the microservice cluster, extract the constraint mapping matrix that maps from the node vector space to the edge vector space, and assemble the Hatch-Dirac operator based on the constraint mapping matrix;

[0088] Furthermore, in S2, the data serialization rules of the microservice cluster are parsed, and the constraint mapping matrix from the node vector space to the edge vector space is extracted, including:

[0089] Intercept the interface definition files between the microservice node and the communication link where the call is associated;

[0090] Lexical analysis is performed on the interface definition file to extract field type dimension constraints and business logic conservation transformation rules from the communication data structure;

[0091] Transform the field type dimension constraints and business logic conservation conversion rules into algebraic mapping relationships;

[0092] Based on algebraic mapping relationships, construct a constraint mapping matrix that determines the linear projection rules from the node vector space to the edge vector space.

[0093] In S2, assembling the Hatch-Dirac operator based on the constraint mapping matrix includes:

[0094] Configure the constraint mapping matrix as a common edge operator to indicate the forward differential evolution of the state vector along the communication link;

[0095] Perform the conjugate transpose operation on the common edge operator to obtain the adjoint boundary operator used to indicate the inverse aggregation evolution of the state vector along the communication link;

[0096] Construct an initial block matrix with zero blocks along the main diagonal;

[0097] The adjoint boundary operator is filled into the first off-diagonal block position of the initial block matrix, and the common edge operator is filled into the second off-diagonal block position of the initial block matrix to generate the Hatch-Dirac operator.

[0098] Specifically, the input receives the interface definition file between the microservice node and the communication link where the call is associated. After processing by the lexical analyzer, algebraic mapping transformation component, and matrix block concatenation module, the output output is the Hatch-Dirac operator.

[0099] The system first intercepts the interface definition file, extracting the field type and dimension constraints, as well as the business logic conservation and transformation rules. (Set up a microservice node.) With communication link Given the existence of call relationships, the system transforms the previously extracted constraints and rules into algebraic mapping relationships, constructing a constraint mapping matrix. .

[0100] Constraint mapping matrix It defines the rules for linear projection from the node vector space to the edge vector space.

[0101] The system will use the constraint mapping matrix Directly configured as a common edge operator Common edge operator It describes the mapping process of the microservice business state vector undergoing forward differential evolution along the communication link.

[0102] For common edge operators Perform conjugate transpose operation to obtain the adjoint boundary operator. Adjoint boundary operator It describes the mapping process of the reverse aggregation and evolution of the service state vector along the communication link.

[0103] Establish an initial block matrix where all matrices in the main diagonal region are zero. Apply the adjoint boundary operator... Write the first off-diagonal block position of the initial block matrix and add the common edge operator. Write the second off-diagonal block position of the initial block matrix to generate the Hatch-Dirac operator. .

[0104] The formula for generating the spliced ​​data is defined as follows:

[0105] ;

[0106] in This represents the Hatch-Dirac operator generated by assembly. Represents zero matrix blocks that match the dimension and operator. This indicates the adjoint boundary operator for performing reverse aggregation evolution. This represents the common edge operator that performs forward difference evolution.

[0107] Conventional automated monitoring platforms only perform simple format checks on interface definition files. When test cases involve complex cross-service data deserialization and state reorganization, conventional code inspection easily misses deep semantic discrepancies. This invention completely strips away the textual attributes of interface files. The dimensional constraints and conservation rules obtained from lexical analysis are physically solidified into matrix mappings. The common edge operator quantifies the state changes imposed by the caller on the callee. The accompanying boundary operator quantifies the theoretical tracing path of these state changes. By constructing a Hatch-Dirac operator assembled from block matrices, a comprehensive measurement tool capable of simultaneously measuring positive data inflow and negative state feedback is essentially built. Relying on the Hatch-Dirac operator, the automated monitoring system is no longer limited to retrieving and comparing raw plaintext logs. It possesses the technical capability to use pure algebraic matrix operations to determine whether data truncation or logical tampering has occurred between microservices, establishing the core of distributed consistency verification.

[0108] S3. Obtain the real-time business state vector group of the microservice cluster and concatenate it into a zero-order co-chain. Use the Hatch-Dirac operator to solve the algebraic Laplace matrix and calculate the quadratic energy functional of the zero-order co-chain under the algebraic Laplace matrix.

[0109] Furthermore, in S3, solving the algebraic Laplace matrix using the Hatch-Dirac operator includes:

[0110] Perform matrix multiplication on the Hatch-Dirac operator to obtain the initial result matrix;

[0111] Extract the non-zero block matrix along the main diagonal from the initial operation result matrix;

[0112] The non-zero block matrix on the main diagonal is established as an algebraic Laplace matrix.

[0113] In S3, calculating the quadratic energy functional of the zero-order cochain under the algebraic Laplace matrix includes:

[0114] Perform a transpose operation on a zero-order cochain to generate a transposed zero-order cochain;

[0115] The intermediate row vector is obtained by performing the first product operation of the transpose of the zero-order colinked matrix and the algebraic Laplace matrix.

[0116] The scalar value is obtained by performing the second product operation between the intermediate row vector and the zero-order co-link.

[0117] The scalar value is established as a quadratic energy functional.

[0118] Specifically, the input receives a real-time business state vector set from the microservice cluster and a pre-assembled Hatch-Dirac operator. After processing by a common-chain concatenation processor, an operator self-multiplication module, and a matrix continuous product component, the output outputs an algebraic Laplace matrix and a quadratic energy functional representing the consistent state.

[0119] The system acquires the business state vectors of each microservice node in the microservice cluster in real time. The system then concatenates and combines the extracted business state vectors of all microservice nodes according to the topological basis order of the distributed cavity layer to generate a zero-order common chain. Zero-order co-chain It belongs to the zero-order same-link space and represents the global state distribution of the entire microservice system at the current discrete point in time.

[0120] For pre-constructed Hatch-Dirac operators The system control processing unit performs matrix multiplication on it to generate an initial result matrix. The formula for the multiplication operation of the Hatch-Dirac operator is defined as follows:

[0121] ;

[0122] in This represents the initial operation result matrix. Represents zero matrix partitioning, Denotes the adjoint boundary operator, This represents the common edge operator.

[0123] The system extracts the non-zero block matrix corresponding to the zero-order state space on the main diagonal of the initial operation result matrix, that is, the block matrix in the upper left corner of the above operation result matrix. The system establishes the non-zero block matrix on the main diagonal as an algebraic Laplace matrix. :

[0124] ;

[0125] in This represents the algebraic Laplace matrix.

[0126] Obtain the aforementioned zero-order co-chain Perform a matrix transpose operation on it to generate a transposed zero-order common chain. The system performs a transpose of the zero-order common chain. With algebraic Laplace matrix The first product operation yields a dimension-reduced intermediate row vector. This intermediate row vector is then further coupled with the original zero-order co-linked vector. The second product operation, involving consecutive multiplications, results in a scalar value that converges to a single scalar value. The system then establishes this scalar value as a quadratic energy functional. The formula for calculating the quadratic energy functional is:

[0127] ;

[0128] in This represents a quadratic energy functional. Indicates a transpose zero-order conchain. This indicates a zero-order co-chain.

[0129] Conventional distributed system testing and monitoring relies on comparing the local logs of individual nodes to troubleshoot inconsistencies. Under high-concurrency stress testing, data interaction between microservices is extremely frequent, making exhaustive comparison of node logs computationally expensive and with very high latency. This solution alters the underlying verification logic. The algebraic Laplace matrix, acting as a geometric diffusion operator on the distributed cavity layer, directly quantifies the degree of deviation in data state between adjacent microservice nodes. Quadratic energy functional operations compress tens of thousands of complex high-dimensional state vectors into a single scalar dimension.

[0130] Based on the principles of algebraic topology, the scalar value of the quadratic energy functional is strictly equal to zero if and only if the data conversion between microservice nodes strictly conforms to the interface serialization constraints and no dirty data is written. Any scalar result greater than zero directly exposes the existence of unsynchronized logical breaks in the system's global topology. The automated monitoring platform, relying on this numerical criterion, avoids the computational burden of retrieving and comparing massive error logs, establishing a low-latency mathematical computation path for discovering split-brain defects in microservice networks.

[0131] S4. When the quadratic energy functional is determined to be greater than zero, an early warning signal is generated. Based on the algebraic Laplace matrix, a discrete layer diffusion dynamics equation is constructed, and the state compensation update matrix is ​​obtained by solving the discrete layer diffusion dynamics equation.

[0132] Furthermore, in S4, the discrete layer diffusion dynamics equation is constructed based on the algebraic Laplace matrix. Solving the discrete layer diffusion dynamics equation yields the state compensation update matrix, which includes:

[0133] Using the geometric constraint of forcing the zero-order co-chain state sequence to project onto the zero-order cohomology group space, a discrete layer diffusion dynamics equation is constructed with the time variable as the independent variable and the negative matrix of the algebraic Laplace matrix as the coefficient matrix.

[0134] Perform eigenvalue decomposition on the algebraic Laplace matrix;

[0135] Extract the eigenvector corresponding to the smallest non-zero eigenvalue from the output of the eigenvalue decomposition operation;

[0136] Using the eigenvector as the steepest descent search direction, the discrete layer diffusion dynamics equation is solved by differential iteration, and the directional components generated by the iterative convergence are extracted to form the state compensation update matrix.

[0137] Specifically, the input receives the triggered warning signal and the pre-calculated algebraic Laplace matrix. After processing by the dynamic equation construction module, eigenvalue decomposition component, and difference iterative solver, the output generates and outputs the state compensation update matrix.

[0138] The system constructs a discrete-layer diffusion dynamics equation with time as the independent variable and the negative matrix of the algebraic Laplace matrix as the coefficient matrix. The geometric constraint for this equation is to force the state sequence of the zero-order co-chain to project onto the zero-order cohomology group space. The algebraic definition of the discrete-layer diffusion dynamics equation is:

[0139] ;

[0140] in This represents the derivative of the zero-order co-linked state sequence with respect to the time variable. Represents a time variable. This represents a sequence of zero-order co-linked states that evolve dynamically over time. represents the negative matrix of the algebraic Laplace matrix.

[0141] The system's algebraic Laplace matrix Perform eigenvalue decomposition. The eigenvalue decomposition formula is expressed as:

[0142] ;

[0143] in The first element obtained by algebraic Laplace matrix decomposition is... 1 eigenvalue, This represents the first corresponding eigenvalue. 1 eigenvector.

[0144] In the obtained full decomposition output, the system removes feature values ​​with zero values ​​and extracts the feature vector corresponding to the smallest non-zero feature value. The smallest non-zero feature value to be extracted is set as... Its corresponding feature vector is .

[0145] The system uses the extracted feature vectors As the steepest descent search direction, the aforementioned discrete layer diffusion dynamics equations are solved using numerical difference iterative methods. The system extracts the directional components generated when the iterative calculation reaches the convergence limit, and integrates all extracted directional components to generate a state compensation update matrix. The equivalent formula for iteratively solving aggregation is expressed as:

[0146] ;

[0147] in This represents the final generated state compensation update matrix. This represents the summation operator. This indicates the current difference iteration step number. This represents the total number of iterations required to reach a convergent steady state. Indicates the step size of the difference iteration. In the eigenvector Under guidance The gradient direction components generated are calculated step by step.

[0148] Conventional automated testing platforms typically handle distributed transaction failures by performing heuristic retries or large-scale brute-force state rollbacks, which can easily cause secondary oscillations in the test network topology and the loss of normal test data. Global state inconsistency in a microservice architecture is essentially a divergence of data flow in the topology. The smallest non-zero eigenvalue of the algebraic Laplace matrix maps the algebraic connectivity of the system in spectral graph theory, and its corresponding eigenvector geometrically indicates the weak links in the topology that cause semantic divergence across the entire network. This step uses this eigenvector to establish the steepest descent search direction for solving the equations, providing a low-cost algebraic evolution path for the system's self-healing. The state compensation update matrix obtained by differential iterative solution avoids blind global data resets and can forcibly pull the deviated test environment back to a logically consistent steady-state space with minimal local register changes, establishing a precise quantitative control method for the microservice split-brain self-healing process.

[0149] like Figure 2 As shown, S5 parses the state compensation update matrix into reverse rollback instructions and cache erase instructions, and sends them to the service mesh data plane proxy component for execution;

[0150] Furthermore, in S5, the state compensation update matrix is ​​parsed into reverse rollback instructions and cache erase instructions, including:

[0151] Iterate through each element of the state compensation update matrix to locate non-zero matrix elements;

[0152] Map non-zero matrix elements to the target microservice node and target communication buffer where state conflicts occur;

[0153] Extract the magnitude and sign characteristics of non-zero matrix elements;

[0154] The amplitude and sign characteristics are translated into a reverse rollback instruction that instructs the target microservice node to restore historical checkpoint business data.

[0155] The amplitude and sign characteristics are translated into a cache erase instruction that instructs the target communication buffer to perform dirty data destruction operations.

[0156] In S5, the execution of the data plane proxy component deployed to the service mesh includes:

[0157] Establish a bypass injection communication channel for the service mesh data plane proxy component deployed at the bottom layer of the microservice cluster;

[0158] The reverse rollback command and cache erase command are pushed to the policy execution module of the service mesh data plane proxy component through the bypass injection communication channel;

[0159] The trigger strategy execution module intercepts application layer network communication traffic across microservice nodes, and in the intercepted state, it performs overwrite operations on the local business status register based on the reverse rollback instruction and the cache erase instruction.

[0160] Specifically, the input receives the state compensation update matrix calculated by the system's pre-processor. After processing by the matrix traversal parser instruction translation engine and the bypass injection channel module, the output sends reverse rollback instructions and cache erase instructions to the target microservice node and communication buffer, ultimately outputting the physical overwrite result of the local business status register.

[0161] The system starts a traversal program to search the state compensation update matrix line by line. The system extracts non-zero matrix elements to form a set of actions to be processed. The mathematical expression for the non-zero element extraction process is as follows:

[0162] ;

[0163] in Represents the set of non-zero matrix elements. The state compensation update matrix represents the first... Each component element.

[0164] The system maps the extracted non-zero matrix elements to the target microservice node and the target communication buffer where a state conflict occurs. The system further separates the magnitude and sign parameters of these non-zero matrix elements. The translation engine generates specific control commands based on the separated magnitude and sign parameters.

[0165] ;

[0166] in This represents the control instruction set generated for the target microservice node and communication cache. The instruction set includes reverse rollback instructions and cache erase instructions. Indicates the instruction mapping transformation operator. This represents compound multiplication of operators. This represents the absolute magnitude characteristic of non-zero matrix elements. The polarity sign characteristic of non-zero matrix elements.

[0167] Upon entering the physical execution phase, the system establishes a bypass injection communication channel directly reaching the underlying layer of the microservice cluster. The previously generated reverse rollback and cache erase commands are pushed into the policy execution module of the service mesh data plane proxy component via this channel. The policy execution module initiates a traffic interception mechanism, blocking application-layer network communication traffic across microservice nodes. In the interception and isolation state, the proxy component performs a forced overwrite operation on the local business state register:

[0168] ;

[0169] in This indicates the value of the local service status register after the overwrite is complete. The value of the local service status register represents the residual dirty data before overwriting. This indicates a register overwrite operator performed based on the control instruction set.

[0170] Conventional distributed transaction rollbacks heavily rely on intrusive business code compensation logic. Application-level retries are highly susceptible to cascading timeout errors when facing concurrent split-brain scenarios in complex topologies. This technical solution decentralizes the repair process to the data plane proxy component layer of the service mesh. Non-zero elements in the matrix precisely locate specific microservice nodes in the network's data flow topology where numerical distortions have occurred. By analyzing amplitude and polarity signs, the system defines the precise execution parameter boundaries for restoring historical checkpoints and destroying corrupted data. Employing a combination of bypass injection of communication channels and application-layer traffic interception, the monitoring system can directly smooth out semantic differences at the underlying register level while preventing further leakage of erroneous data. The operation avoids any modification to the core business logic code of the microservices, achieving automated physical repair without code intrusion and establishing a final-state self-healing control mechanism to address microservice concurrent split-brain defects.

[0171] Example 2:

[0172] In high-concurrency stress testing scenarios across the entire supply chain of major e-commerce platforms before major sales events, the test environment typically deploys tens of thousands of complex microservice nodes, including order processing, payment gateways, and warehousing and logistics. When the system encounters a surge of tens of millions of concurrent traffic, hidden cross-service node business state conflicts often arise due to underlying network jitter or distributed transaction synchronization delays. These conflicts can manifest as order nodes showing payment but inventory nodes not performing deductions. In such scenarios, existing monitoring methods can only extract basic hardware indicators such as node liveness, CPU load, or simple binary call connectivity. They cannot penetrate the application layer to accurately identify global semantic split-brain and hidden dirty data write issues caused by data serialization and protocol conversion failures between microservices. Once distributed state divergences occur, the lack of quantitative measurement and automated reconciliation mechanisms for business logic consistency makes the test system prone to cascading errors and deep contamination of test data. Testers are forced to interrupt the process, spending a significant amount of time manually checking the link logs and performing a crude global database rollback, leading to a break in the continuity of automated stress testing and a sharp increase in testing costs. To address the aforementioned problems, this invention provides an automatic monitoring system for a server testing environment, the structure of which is as follows: Figure 3 As shown. The specific implementation process of this system is as follows:

[0173] The server testing environment automated monitoring system establishes a complete hardware execution mapping architecture between the physical microservice network and the abstract algebraic topology. The topology foundation construction module transforms the underlying microservice nodes and communication links into a distributed cellular layer foundation. The operator mapping configuration module extracts the business logic conservation rules from the interface definition and physically solidifies them into a Hatchdirac operator with measurement capabilities. The energy functional monitoring module performs algebraic Laplace matrix operations on zero-order co-links based on the operators, compressing massive discrete distributed business states into a single quadratic energy functional scalar that determines global semantic split-brain. After confirming a state conflict, the diffusion equation solving module derives the discrete layer diffusion dynamics equation and outputs a state compensation update matrix that forces the system to return to consistency. The closed-loop compensation execution module is responsible for parsing the matrix elements in the algebraic space into reverse rollback instructions and cache erase instructions for specific cache areas, which are then physically intercepted and overwritten by the service mesh data plane proxy component. The entire system constructs a technical chain from topology awareness and operator measurement to gateway bypass execution through a module-connected data flow, providing a machine-automated reconciliation entity for resolving semantic conflicts in distributed concurrent testing.

[0174] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended 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 described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatically monitoring a server testing environment, characterized in that, Includes the following steps: S1. Collect the directed call topology of the microservice cluster in the server test environment and construct a distributed cellular layer with node vector space and edge vector space. S2. Parse the data serialization rules of the microservice cluster, extract the constraint mapping matrix that maps the node vector space to the edge vector space, and assemble the Hatch-Dirac operator based on the constraint mapping matrix; S3. Obtain the real-time business state vector group of the microservice cluster and concatenate it into a zero-order co-chain. Use the Hatch-Dirac operator to solve the algebraic Laplace matrix and calculate the quadratic energy functional of the zero-order co-chain under the algebraic Laplace matrix. S4. When the quadratic energy functional is determined to be greater than zero, an early warning signal is generated. Based on the algebraic Laplace matrix, a discrete layer diffusion dynamics equation is constructed, and the state compensation update matrix is ​​obtained by solving the discrete layer diffusion dynamics equation. S5. The state compensation update matrix is ​​parsed into reverse rollback instructions and cache erase instructions, and sent to the service mesh data plane proxy component for execution.

2. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S1, the directed call topology of the microservice cluster in the acquisition server test environment, constructing a distributed cellular layer with node vector space and edge vector space includes: Intercept concurrent request tracing logs between microservice nodes in the microservice cluster; Based on the concurrent request tracing logs, extract the calling microservice node, the called microservice node, and the communication link between them, and construct a finite graph topology base space composed of a set of microservice nodes and a set of communication links; Extract the internal business state variable dimension of each microservice node in the microservice node set, and assign the node vector space with matching dimension to the corresponding microservice node; Extract the application interface load protocol specification of each communication link in the communication link set, and assign the edge vector space with matching dimensions to the corresponding communication link; The distributed cellular layer is generated by combining the finite graph topology base space, the node vector space, and the edge vector space through an algebraic structure mapping.

3. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S2, parsing the data serialization rules of the microservice cluster and extracting the constraint mapping matrix from the node vector space to the edge vector space includes: Intercept the interface definition files between the microservice node and the communication link where the call is associated; Lexical analysis is performed on the interface definition file to extract field type dimension constraints and business logic conservation transformation rules from the communication data structure; Transform the field type dimension constraints and the business logic conservation conversion rules into an algebraic mapping relationship; Based on the algebraic mapping relationship, construct the constraint mapping matrix that determines the linear projection rule from the node vector space to the edge vector space.

4. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S2, assembling the Hatch-Dirac operator based on the constraint mapping matrix includes: The constraint mapping matrix is ​​configured as a common edge operator to indicate the forward differential evolution of the state vector along the communication link; Perform a conjugate transpose operation on the common edge operator to obtain an adjoint boundary operator used to indicate the reverse aggregation evolution of the state vector along the communication link; Construct an initial block matrix with zero blocks along the main diagonal; The adjoint boundary operator is filled into the first off-diagonal block position of the initial block matrix, and the common edge operator is filled into the second off-diagonal block position of the initial block matrix to generate the Hatch-Dirac operator.

5. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S3, solving the algebraic Laplace matrix using the Hatch-Dirac operator includes: Perform matrix multiplication on the Hatch-Dirac operator to obtain the initial result matrix; Extract the non-zero block matrix along the main diagonal from the initial operation result matrix; The non-zero block matrix along the main diagonal is established as the algebraic Laplace matrix.

6. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S3, calculating the quadratic energy functional of the zero-order cochain under the algebraic Laplace matrix includes: Perform a transpose operation on the zero-order cochain to generate a transposed zero-order cochain; The intermediate row vector is obtained by performing the first product operation between the transposed zero-order cochain matrix and the algebraic Laplace matrix. The scalar value is obtained by performing a second product operation between the intermediate row vector and the zero-order co-linked vector. The scalar value is established as the quadratic energy functional.

7. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S4, the step of constructing the discrete layer diffusion dynamics equation based on the algebraic Laplace matrix and solving the discrete layer diffusion dynamics equation to obtain the state compensation update matrix includes: Using the geometric constraint of forcing the state sequence of the zero-order co-chain to project onto the zero-order cohomology group space, the discrete layer diffusion dynamics equation is constructed with the time variable as the independent variable and the negative matrix of the algebraic Laplace matrix as the coefficient matrix. Perform eigenvalue decomposition on the algebraic Laplace matrix; Extract the eigenvector corresponding to the smallest non-zero eigenvalue from the output of the eigenvalue decomposition operation; Using the eigenvector as the steepest descent search direction, the discrete layer diffusion dynamics equation is solved by differential iteration, and the directional components generated by the iterative convergence are extracted to form the state compensation update matrix.

8. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S5, parsing the state compensation update matrix into reverse rollback instructions and cache erase instructions includes: Traverse each element of the state compensation update matrix to locate non-zero matrix elements; Map the non-zero matrix elements to the target microservice node and the target communication buffer where a state conflict occurs; Extract the magnitude and sign characteristics of the non-zero matrix elements; The amplitude and symbol characteristics are translated into a reverse rollback instruction that instructs the target microservice node to restore historical checkpoint business data. The amplitude and symbol characteristics are translated into a cache erase instruction that instructs the target communication cache to perform a dirty data destruction operation.

9. The automatic monitoring method for a server testing environment according to claim 1, characterized in that, In S5, the execution of sending the data to the service mesh data plane proxy component includes: Establish a bypass injection communication channel for the service mesh data plane proxy component deployed at the bottom layer of the microservice cluster; The reverse rollback instruction and the cache erase instruction are pushed to the policy execution module of the service mesh data plane proxy component through the bypass injection communication channel; The policy execution module is triggered to intercept application layer network communication traffic across microservice nodes, and in the intercepted state, the local business status register is overwritten based on the reverse rollback instruction and the cache erase instruction.

10. An automatic monitoring system for a server testing environment, characterized in that, The automatic monitoring method for a server testing environment according to any one of claims 1-9 includes the following modules: The topology base construction module is used to collect the directed call topology of the microservice cluster in the server test environment and construct a distributed cell layer with node vector space and edge vector space. The operator mapping configuration module is used to parse the data serialization rules of the microservice cluster, extract the constraint mapping matrix that maps the node vector space to the edge vector space, and assemble the Hatch-Dirac operator based on the constraint mapping matrix. The energy functional monitoring module is used to obtain the real-time business status vector group of the microservice cluster and concatenate it into a zero-order co-chain. It uses the Hatch-Dirac operator to solve the algebraic Laplace matrix and calculates the quadratic energy functional of the zero-order co-chain under the algebraic Laplace matrix. The diffusion equation solving module is used to generate an early warning signal when the quadratic energy functional is determined to be greater than zero, construct a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, and solve the discrete layer diffusion dynamics equation to obtain the state compensation update matrix. The closed-loop compensation execution module is used to parse the state compensation update matrix into reverse rollback instructions and cache erase instructions, and send them to the service mesh data plane proxy component for execution.

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