190 results about "Reverse analysis" patented technology

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention relates to the technical field of mathematical modeling, in particular to a mathematical model modeling method based on data analysis operation processing. The method comprises the following steps: collecting metal pollution data of a target water area by using a multi-parameter water quality sensor array; performing time sequence alignment on the metal pollution data to generate a standardized pollution data set; constructing a metal migration model based on fluid dynamics and chemical adsorption coupling; inputting the standardized pollution data set into the metal migration model for three-dimensional space interpolation calculation, and generating a metal pollutant diffusion cloud picture; pollution source reverse analysis is carried out on the metal pollutant diffusion cloud picture, and a potential pollution source coordinate set and pollution contribution degree sorting are generated. According to the method, by integrating multi-parameter water quality sensor data, fluid dynamics and chemical models and multispectral analysis, the modeling precision of the water pollution diffusion mathematical model is improved.

The invention provides an unmanned aerial vehicle communication link cracking method and system based on a chaotic modulation waveform. The method comprises the following steps: generating a receiving signal matrix by using a mixed index multi-carrier chaotic signal, carrying out separation operation on an index bit characteristic and a modulation bit characteristic which are mixed and superposed in the matrix, generating an independently modulated carrier component set, and then adjusting a beam forming direction of a multi-channel antenna array according to a frequency band distribution range of the set. The method comprises the following steps: generating a carrier component signal, then performing matrix reconstruction on the carrier component signal, removing environmental noise components, extracting a corresponding chaotic waveform base vector, and finally generating a spreading code sequence according to a modulation symbol mapping relation between the chaotic waveform base vector and a target unmanned aerial vehicle; according to the technical scheme provided by the invention, the problems that a traditional method is poor in adaptability to aperiodic chaotic signals and insufficient in hybrid feature decoupling precision are solved, the anti-interference capability of target signals is improved, and efficient reverse analysis and spreading code accurate reconstruction of an unmanned aerial vehicle communication protocol in a complex electromagnetic environment are realized.

The invention discloses a distributed household photovoltaic production data access method comprising the following steps: obtaining a unique identifier of a distributed household photovoltaic device, the unique identifier comprising a communication protocol association feature; judging whether a communication rule corresponding to the unique identifier is included in a preset communication protocol feature library of the cloud server or not; if the preset communication protocol feature library comprises a communication rule corresponding to the unique identifier, performing data communication with the distributed household photovoltaic equipment based on the communication rule; if the preset communication protocol feature library does not include the communication rule corresponding to the unique identifier, performing reverse analysis on original communication data of the distributed household photovoltaic equipment, constructing a structured communication protocol template, and performing data communication with the distributed household photovoltaic equipment based on the structured communication protocol template; and the cloud server is in data communication with a plurality of distributed household photovoltaic devices.

The invention discloses a soft tissue constitutive parameter calibration method in a soft tissue-skeleton bird body model for bird strike simulation, and the method comprises the construction of a soft tissue-skeleton bird body finite element model and finite element reverse analysis based on flat bird strike. Dividing wing bones, leg bones and head and neck parts in the bones of the bird body into hexahedral unit grids, dividing wishbones, pecking bones, fossil protrusions and ribs into shell units, and dividing soft tissues into SPH particles; further obtaining bone elastic-plastic constitutive parameters through a static compression test, and endowing the bone units with the bone elastic-plastic constitutive parameters; an objective function is set as a mean square error between a flat plate center point pressure-time curve obtained by a real bird body impact flat plate test and corresponding curve data calculated by soft tissue-skeleton bird body impact flat plate finite element simulation, and soft tissue constitutive model parameters are inversely calibrated by adopting a genetic algorithm; and model and material parameter support is provided for bird strike simulation of a high-fidelity aircraft structure.

The invention provides an instant conference evidence obtaining method and device based on memory reverse analysis. The method comprises the following steps: acquiring a target memory image; analyzing the target memory image by using a preset conference metadata reverse extraction algorithm to obtain conference metadata; the conference metadata reverse extraction algorithm comprises the following steps: identifying process information of an instant conference in a memory image, extracting and dumping files related to the process information in a memory, and analyzing the dumped memory files to extract conference metadata; scanning and matching the memory image by using a preset conference data stream engraving algorithm to obtain conference data stream information; the conference data flow library adjusting algorithm comprises the steps of reconstructing a data structure of a text message and / or a file message transmitted in an instant conference, identifying and positioning a key data structure needing evidence obtaining in the memory mapping according to the reconstructed data structure, and extracting conference data flow information from the positioned key data structure.

The invention discloses a humanoid robot navigation method, device and equipment based on defect complementation, and the method comprises the steps: extracting, recognizing and measuring defect points and data cavity regions through multi-sensor defect features, and generating a defect feature map; establishing a confidence decreasing verification mechanism based on defect mutual exclusion analysis, determining sensor weight distribution, and generating a navigation gain parameter from a negative error feature set; performing reverse analysis on the navigation data to extract data back information, reconstructing environment implicit features and constructing a reverse navigation field; analyzing the spatial distribution density of the data void region to generate a void density map, dividing a path phase change region and generating a phase change rule set; and fusing the curve path parameters, the three-dimensional path parameters and the navigation gain parameters to generate a self-adaptive navigation sequence, and driving the humanoid robot to realize intelligent navigation control. According to the method, the sensor defects are converted into navigation resources, and the navigation performance in a complex environment is improved.

The invention relates to a fault diagnosis method and device in a PLC system, the PLC system and a storage medium, and belongs to the technical field of industrial automation control. The method comprises the following steps: acquiring a starting signal and a plurality of shutdown fault signals of equipment in real time; when it is detected that the starting signal jumps from starting to stopping, a reverse analysis process is triggered, and multiple shutdown fault signals are polled according to a preset priority sequence; if a certain shutdown fault signal is in a fault triggering state, generating a corresponding fault code, and keeping the duration of the fault code to be not less than a preset duration through a timer; and transmitting the fault code to the HMI for display, and recording the fault code to a PLC internal fault log database. According to the invention, through embedded real-time diagnosis and a reverse trigger mechanism, the real-time performance of fault capture and the automation of diagnosis are realized, and the efficiency of fault diagnosis is improved. And meanwhile, the diagnosis function block is only activated when the equipment is shut down, so that the CPU occupancy rate is greatly reduced.

The invention discloses a de-signed binary code reverse analysis method based on library function matching. The method comprises the following steps of: obtaining de-signed binary codes and a candidate library file list thereof in the embedded equipment, and extracting a library function and information of each object file of each candidate library file; performing matching operation on each object file in sequence to establish a library function matching table; and acquiring addresses for calling other library functions in each library function, performing consistency comparison with the addresses of the successfully matched library functions, and confirming the calling relationship of the library functions, thereby determining the function of the de-signed binary code in the embedded equipment. According to the method, the library function in the de-signed binary code can be accurately identified, the calling relation is checked, and the method is suitable for component analysis and traceability of scenes such as computer programs, embedded firmware and DSP programs so as to accurately identify the function of the de-signed binary code in embedded equipment.

The invention discloses a dosing method and system based on time sequence constraint and priori knowledge enhancement and a storage medium, and belongs to the technical field of water quality regulation and control, the method comprises the following steps: firstly, deriving features based on a lag operator, a future operator, an interaction operator and a rolling statistical operator in a pre-stage and an intervention stage through correlation analysis under the constraint of a time sequence; and finally, keeping the features which are highly related to the target variables in the result stage. A prediction model is constructed based on an XGBoost model, and feature weights of key variables are dynamically adjusted based on priori knowledge of chemical reactions and physical dynamics. In a reverse analysis stage of the prediction model, solving a unique group of dosing suggestion values, and integrating the dosing suggestion values to form a future dosing sequence; and outputting a corresponding dosing strategy based on the future dosing sequence. According to the method, the time sequence characteristics and the chemical and physical priori knowledge are combined, the prediction model with high interpretability and high prediction precision is constructed, and the accuracy and foresight of the dosing strategy are effectively improved.

The invention discloses a cascade operation state real-time monitoring and anti-collision risk intelligent early warning method and system, and the method comprises the steps: obtaining multi-sensor data, determining a key monitoring node, and extracting parameter changes to form an operation state trend sample; carrying out edge calculation processing on the trend sample, reversely analyzing and reconstructing component state parameters from the vibration spectrum signal, generating a step gap change curve, matching with a standard model to obtain a risk characteristic probability value, and generating a state traction chain according to the risk characteristic probability value; obtaining a displacement distribution coefficient and a vibration speed index from the state drag chain, predicting the collision probability distribution of the displacement distribution coefficient through cloud deep learning, determining an early warning level in combination with the vibration speed index, and generating early warning optimization configuration; emergency distribution is determined through linkage control analysis, differentiated response areas are identified, and linkage optimization factors are extracted from the differentiated response areas; finally, differential control parameters are generated and matched with the state traction chain to generate an early warning signal, and intelligent abnormal recognition and self-adaptive early warning of the step equipment are achieved.

According to the intelligent terminal equipment intelligent adaptation method and device, intelligent terminal automatic discovery is carried out based on scene-based adaptive ARP scanning and multi-fingerprint UPnP filtering, access equipment is obtained, automatic and precise discovery of intelligent terminal equipment is achieved, and the intelligent terminal equipment can be automatically and accurately found by standardizing the scanning frequency and the filtering rule. The equipment discovery efficiency and accuracy are improved, the access equipment is subjected to prefabricated API library matching and AI reverse analysis to obtain interface information of the access equipment, automatic analysis of a standard interface and a private interface is realized based on a multi-dimensional analysis technology of an AI large language model, and the equipment discovery efficiency and accuracy are improved by utilizing the AI large language model and combining a built-in multi-language programming template. According to the method, the adaptive code matched with the interface information is automatically compiled, the adaptive code is automatically generated based on the AI technology, the compatibility of multi-protocol and multi-interface type equipment is supported, and finally the core problems of low efficiency, poor compatibility, difficult maintenance and high threshold in the existing intelligent terminal adaptation process are solved.

The invention relates to the technical field of file one-way import, and discloses a multi-protocol file one-way import method, which comprises the following steps: S1, protocol dynamic analysis: based on protocol fingerprint features and a machine learning model, identifying a protocol type of a source end input data stream, and generating a protocol adaptation rule; s2, intelligent fragmentation of the file: dynamically calculating a fragmentation strategy according to a protocol type and a network state, splitting the file into a plurality of fragments and distributing the fragments to different protocol transmission channels; s3, multi-protocol parallel transmission is carried out; s4, performing adaptive fault-tolerant recovery; and S5, recombining and checking the target end. According to the invention, through the protocol fingerprint feature extraction and the hybrid neural network model, and through capturing the message header flag bit, the encryption algorithm identifier and the time sequence interaction behavior in the protocol handshake stage, accurate identification of multiple protocol types and reverse analysis of the private protocol are realized, and the anti-interference capability and the dynamic adaptation efficiency of protocol identification are improved.

The invention relates to the technical field of Web security and artificial intelligence, and discloses a Web reverse analysis method based on a large model and dynamic feedback, which comprises the following steps: constructing an AST-LLM joint analysis engine, defining an AST node importance evaluation formula, carrying out weight quantification on AST nodes, and screening out key functions or code snippets in reverse analysis; semantic analysis is carried out through LLM in combination with context, and candidate names of confusion variables are generated; semantic similarity calculation is carried out on the key nodes screened out through weight quantification and candidate variable names generated by LLM, and semantic grouping is carried out on confusion variables associated with AST nodes through a spectral clustering algorithm; a code structure is optimized through AST node remapping, and codes are corrected through real-time interaction; a closed-loop optimization system of LLM and log instrumentation is constructed, and an optimization engine is dynamically fed back; the method has the advantages that multi-state confusion is resisted, the semantic consistency of reverse output and original logic is ensured, the problem of reverse analysis distortion in a dynamic confusion scene is solved, and the readability, the performability and the maintainability of codes are kept.

The invention relates to the technical field of digital twinning and environment control, and discloses an intelligent office space management method based on a digital twinning technology, which comprises the following steps: constructing a lumped parameter network model to calculate theoretical state data, calculating state residual vectors of actually measured data and theoretical data, and calculating the state residual vectors of the actually measured data and the theoretical data; the residual error is decoupled into a transient load disturbance component and a structure parameter drift component through a dual-band filter, the transient component is reversely projected into an equivalent disturbance parameter injection model by using a model inverse dynamics principle for advanced deduction, and a model structure parameter matrix is identified and corrected by using a gradual state component; according to the method, through frequency domain decoupling and reverse analysis of the state residual error, software-defined perception of an unmeasurable hidden load and internal adaptive correction of model aging drift are realized, response lag of a large thermal inertia system is avoided, and the self-evolution ability of the system in the whole life cycle is improved without extra hardware investment.

The invention discloses an advanced network threat intelligent detection and defense system and method in the technical field of network defense. The system comprises a data acquisition module which analyzes encrypted traffic through a deep packet inspection technology and marks space-time metadata; the feature extraction module is used for constructing a node behavior association graph by adopting a multi-scale sliding window and a graph neural network; the detection engine module integrates multi-model parallel analysis of LSTM, random forest and the like, and dynamically fuses detection results through an entropy weight method; and the defense execution module is used for realizing flow cleaning and virtual trapping node deployment based on an SDN (Software Defined Network) technology. The method comprises the steps of protocol extension field reverse analysis, covert channel time sequence correlation analysis and dynamic network topology adjustment. According to the method, the encryption APT attack detection accuracy reaches 98.7%, the false alarm rate is reduced to 0.15%, the defense strategy effective time is shorter than 1 second, and the method is suitable for complex network environments such as cloud computing and industrial Internet of Things.

The invention discloses a comprehensive energy supply system reliability detection method considering state reverse analysis. The method comprises the following steps: firstly, constructing an energy conversion model of a multi-energy coupling element in a comprehensive energy supply system; thirdly, constructing an output power model of the multi-energy coupling element based on the energy conversion model of the multi-energy coupling element and initial input energy of the comprehensive energy supply system; and finally, the reliability of the comprehensive energy supply system is rapidly detected by using the output power model of the multi-energy coupling element. The method provided by the invention focuses on the component combination of which the output power is smaller than the energy demand to realize the rapid evaluation of the reliability of the comprehensive energy supply system.

The invention discloses a self-aligning roller bearing rotation flexibility test data and assembly parameter correlation analysis method and a self-aligning roller bearing rotation flexibility test data and assembly parameter correlation analysis system. According to the method, bearing assembly parameters and multi-dimensional time sequence data of a rotation test are acquired through a system, and a quantitative correlation model between the bearing assembly parameters and the multi-dimensional time sequence data is constructed by using a machine learning algorithm. On the basis of the model, reverse analysis can be carried out on abnormal test data, and an assembly deviation source which causes poor flexibility is accurately positioned; target parameters can be input in the process design stage, and the expected performance of the bearing can be predicted in the forward direction. The corresponding system integrates a data acquisition module, a model analysis module, an application output module and a process feedback control module, and is integrated with a production line control system. According to the method, traditional qualitative judgment depending on experience is converted into quantitative analysis driven by data and models, intelligent closed loop from quality detection and root diagnosis to process optimization is achieved, and the consistency of bearing assembly quality and the intelligent level of process control are remarkably improved.

The invention relates to a method and a system for capturing and analyzing industrial network protocol data packets, which solve the technical problem that the existing industrial protocol analysis technology is poor in adaptability to unknown, private or non-standard protocols, and the method comprises the following steps: capturing and screening network data packets for protocol identification. If the protocol is the known protocol, calling a preset analyzer for analysis; if the protocol is an unknown protocol, extracting a protocol feature vector of the protocol, obtaining a candidate protocol and confidence through reasoning of a neural mapping model, and dynamically judging whether to start interactive reasoning to obtain user feedback, calibrating a parameterized protocol template and instantiating to generate an executable analysis rule according to the structural matching degree of an existing analyzer; and finally, registering and using the new analyzer to realize adaptive learning and analysis. The method has the following effects: intelligent identification of an unknown protocol, dynamic construction of an analyzer and system self-expansion are autonomously realized through an adaptive learning mechanism, and normal form upgrading from manual reverse analysis to system autonomous evolution is completed.

The application discloses a humanoid robot navigation method and device based on defect complementarity, and an equipment. Defect points and data hollow areas are measured and identified through multi-sensor defect feature extraction and recognition, and a defect feature atlas is generated. A confidence decreasing verification mechanism is established based on defect mutual exclusion analysis, sensor weight distribution is determined, and a navigation gain parameter is generated from a negative error feature set. Data back information is extracted through reverse analysis of navigation data, environment implicit features are reconstructed, and a reverse navigation field is constructed. A hollow density map is generated by analyzing the spatial distribution density of data hollow areas, a phase transition area of a path is divided, and a phase transition rule set is generated. An adaptive navigation sequence is generated by fusing a curve path parameter, a three-dimensional path parameter and a navigation gain parameter, and a humanoid robot is driven to realize intelligent navigation control. The application converts sensor defects into navigation resources, and improves navigation performance in a complex environment.

The invention discloses a bit-level CAN (Controller Area Network) data load reversing method and device based on hierarchical clustering. The method comprises the following steps: acquiring original CAN message data in a vehicle running process through a CAN data acquisition tool, and processing the CAN message data by utilizing a frequency matrix, a probability matrix, a bit flipping rate and a hierarchical clustering method to obtain CAN message signal characteristics; performing reverse analysis on the CAN message signal by analyzing the characteristics of the CAN message signal and combining the signal type of the automobile to obtain a reverse analysis result; and controlling the functions of the automobile based on the reverse analysis result. According to the method and the device, the problem of dependence on special equipment and a database (DBC file) private by a manufacturer in the prior art is solved, and the technical problem of more accurate and finer-grained signal automatic reverse analysis is realized.

The application relates to the technical field of resource scheduling, in particular to an enterprise resource scheduling management method and system based on big data. The method comprises the following steps: acquiring and determining process parameters and a delivery deadline of a demand product based on order information, and performing reverse analysis on the process parameters to obtain a production mode of the demand product; the production mode comprises a demand process and corresponding demand load; calling production planning of an enterprise section corresponding to the demand process based on the delivery deadline, and analyzing a load trend of the enterprise section based on the production planning through a preset big data model to generate a dynamic constraint baseline; in response to the case that the demand load meets the dynamic constraint baseline in the production planning, first allowed calling prompt information is generated; the first allowed calling prompt information is used for indicating that production resources of the enterprise section are allowed to be called; and the method can effectively solve the problem of poor scheduling accuracy.

The invention discloses a privacy compliance analysis method for a built-in third-party library of a mobile application, and belongs to the field of mobile application dynamic analysis, automatic testing and privacy compliance analys.The method comprises the steps that firstly, dynamic testing is conducted on an APP through a dynamic analysis module, API calling is monitored and flow and log files of API calling are stored while flow plaintexts are collected and decrypted, and the flow plaintexts are stored; carrying out reverse analysis on the APP on the basis of analyzing the flow and the log file to identify a data flow of a built-in third-party library of the APP; step 2, the privacy policy analysis module uses a large language model to preprocess the form of the privacy policy of the APP, identifies a list of a built-in third-party library contained in the form and a corresponding privacy information type, and then uses a natural language processing method to extract behaviors related to the privacy information from the privacy policy of the APP; and step 3, a privacy compliance analysis module compares a result of the dynamic analysis module in the step 1 with a result of the privacy policy analysis module in the step 2 for the defined illegal behaviors, and detects a privacy compliance problem of a built-in third-party library TPL of the APP.

The invention provides a key node identification method and device in an industrial control system, equipment and a medium. The key node identification method comprises the steps of performing reverse analysis on firmware codes of control equipment to obtain a control logic configuration file; disassembling the control software firmware code to obtain a software disassembling code; performing code labeling, code slicing and code translation on the software disassembly code to obtain a class C pseudo code; performing code analysis on the class C pseudo code to obtain a software structure configuration file; performing file analysis on the control logic configuration file and the software structure configuration file to obtain a service logic relationship between the control equipment and the control software and a service logic relationship between the control equipment and the field equipment; constructing a business logic map based on the business logic relationship between the control equipment and the control software and the business logic relationship between the control equipment and the field equipment; and identifying a key control link in the service logic map, and carrying out key node positioning on the key control link. Therefore, key nodes are effectively positioned.

The invention relates to the technical field of file management, in particular to a file cabinet internet-of-things access control and data tracing method. The method comprises the following steps: firstly, generating a multi-site coupling digital key by extracting identity, authority and terminal characteristics of a borrower; during access verification, a dynamic instruction is used as a logic mask to extract effective site features, and a cabinet door is opened when the effective site features are consistent; then, synthesizing a preorder fingerprint with the identity residue of the borrower, writing the preorder fingerprint back to the physical tag, and generating a progressive fingerprint chain by adopting a cascade hash algorithm in a circulation process; and finally, disassembling the fingerprint chain through a reverse analysis model, and restoring the identity of the borrower in each link in combination with a cosine similarity matching algorithm. According to the method, the problems that offline circulation of the archives is non-traceable, authority verification is easy to crack and data highly depends on a central database are solved, and decentralized auditing and responsibility traceability of the archives in the whole life cycle are achieved.

The invention provides a data rule management method and system, electronic equipment and a medium, and the method comprises the steps: obtaining operation information of a user on rule dimensions through a visual editing interface, and obtaining an initial combination rule; performing forward disassembly on the initial combination rule to obtain a normalized atomic item set; performing coverage and conflict detection on the standardized atomic item set to obtain a detection result; and if the detection result is normal, generating an atomic item change set based on the detection result, storing the atomic item change set to an atomic item database, and performing reverse analysis based on the atomic items in the atomic item database to obtain a target combination rule. According to the method, the consistency and the traceability of the data rules in the editing, storing and issuing processes can be ensured.

The application discloses a reverse analysis method based on disassembly function recovery, which is used for solving the embedded reverse analysis process including satellite firmware embedded operating system and the like. Through a multi-step machine learning process, the method combines disassembler output and actual binary data to achieve efficient analysis of embedded operating system firmware. The specific steps include evaluating the effectiveness of the disassembler, creating training data, using neural networks to integrate multiple disassembler outputs to improve analysis accuracy, and further optimizing prediction performance through a context-aware integration model. The application can improve the accuracy and efficiency of reverse analysis, and has advantages in bypassing obfuscation mechanisms, improving string matching accuracy, and handling encrypted or special file systems.

The embodiment of the present application provides a display device and a display terminal, the display device comprises: a circuit board and a display driving chip; at least one target wire is embedded in the circuit board, a target test point on the target wire is arranged between two ends of the target wire and located on the surface of the circuit board, the target test point is covered with an insulating layer, and each target wire is connected with at least one output end of the display driving chip; after the insulating layer covering the target test point of any target wire is scraped off and the any target wire is disconnected from the position where the target test point is arranged, the output end connected with the any target wire in the display driving chip works abnormally. Therefore, the reverse analysis protection of the display device is realized, and the difficulty of reverse analysis of the display device is improved.

The invention belongs to the technical field of vulnerability detection, and relates to a firmware analysis and network scanning combined industrial control equipment vulnerability detection method and system. The method comprises the following steps: extracting a component static fingerprint of the industrial control equipment through firmware reverse analysis; obtaining a dynamic fingerprint of the industrial control equipment through network traffic scanning; performing cross validation on the static fingerprint and the dynamic fingerprint of the component, removing unreachable vulnerabilities and finding hidden vulnerabilities to obtain a component-level potential vulnerability information table; detecting and verifying a hard coded password and a weak password of the industrial control equipment based on a cross verification result to form a hard coded password list and a weak password list; and according to the component-level potential vulnerability information table, the hard coded password list and the weak password list, evaluating vulnerability validity, and forming a structured vulnerability list. According to the method, two technologies of firmware analysis and network scanning are effectively combined, so that the false alarm rate and the missing report rate in the vulnerability detection process of the industrial control equipment can be remarkably reduced, and the detection capability of specific types of vulnerabilities is improved.

The invention discloses a version identification method and system for binary software, and belongs to the technical field of computer program detection. The method comprises the following steps: carrying out reverse analysis on a binary file, and extracting a character string and a constant serving as a function return value; performing layered filtering on the character string and the constant to screen out candidate version semantic features possibly containing version semantic information, and extracting a context environment of the candidate version semantic features in a binary file; performing semantic enhancement on the candidate version semantic features in combination with a context environment of the candidate version semantic features in a binary file; and performing version identification on the binary file according to the character string and the enhanced semantic features of the candidate versions to obtain a version identification result of the binary file. According to the method, the identification problem of the binary software multiplexing open source software version can be effectively solved, and the identification accuracy and the recall rate are improved.