1719results about How to "Increase diversity" patented technology

The invention relates to the technical field of multi-beam sounding calibration, and discloses a multi-beam sounding online real-time calibration method. The method comprises the following steps: acquiring original water depth data and corresponding sensor attitude data in real time; generating a data quality evaluation result containing a data credibility index and a data abnormal distribution feature based on the original water depth data; triggering a dynamic calibration mechanism containing a calibration period adaptive adjustment strategy and a calibration parameter optimization rule according to the data credibility index; optimizing the target calibration rule parameters in combination with the sensor attitude data to generate an optimized calibration parameter set, and correcting the original water depth data in real time by using the parameter set to generate primary calibration data; and performing topographic feature consistency check on the primary calibration data, if the primary calibration data does not pass the topographic feature consistency check, starting a re-calibration process containing a rule base updating mechanism and a parameter iteration mechanism, and analyzing a deviation trend based on historical calibration records to generate a system error prediction model.

The invention belongs to the field of wastewater treatment and industrial automatic control, and particularly relates to an intelligent dosing control method based on water quality online monitoring. The method comprises the following steps: capturing a water body spectrum fingerprint spectrum by using a full-spectrum scanning module to obtain a characteristic signal sequence; a chemical informatics molecular algorithm is introduced for preprocessing and feature enhancement, and molecular fingerprint information is recognized; a convolutional neural network model is constructed to decouple chemical oxygen demand, ammonia nitrogen, total phosphorus and water toxicity indexes from the spectral signals, and multi-parameter parallel inversion is achieved; and based on the inversion index and the spectral dynamic trend, the dosage is calculated through an intelligent decision-making algorithm, and accurate dosing of the medicament is driven. According to the invention, through fusion of full spectrum scanning and deep learning, high-precision anti-interference water quality perception is realized, potential risks can be identified and preventive intervention can be carried out, and the intelligent level and economical efficiency of dosing control are improved.

The invention provides a multi-path information retrieval method and system based on a language model and uncertainty evaluation. The multi-path information retrieval method comprises the following steps: receiving a user query; performing semantic analysis on user query by using a pre-trained language model so as to output a quantitative uncertainty index in an unsupervised manner; dynamically determining a quantity ratio for controlling query generation and a balance parameter for controlling result sorting based on the index; based on the user query and the strategy parameters, generating and executing two groups of differential queries at least comprising exploratory queries and utilizable queries to obtain multiple paths of retrieval results; and combining multiple paths of results, and carrying out dynamic balance reordering between the correlation and diversity of the results according to balance parameters by adopting an ordering mechanism. According to the method, the retrieval strategy can be adaptively adjusted according to the uncertainty of query, and the coverage degree and diversity of results are remarkably improved.

The application provides a consumer rights protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language processing. The method comprises: preprocessing a text to be reviewed to obtain structured text feature data; using a pre-trained large language model to perform semantic understanding on the structured text feature data to obtain a text semantic vector containing domain semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base to identify and output a risk element set; and performing compliance determination based on the risk element set to obtain a preliminary review conclusion containing a risk level, evidence sufficiency, rule matching degree and benchmark confidence. The application automatically converts an original text into a high-confidence review conclusion with explainability through intelligent processing of the whole process.

The invention provides a target identification method and system based on multi-source information fusion, and relates to the technical field of low-altitude target detection. The method comprises the following steps: acquiring target radar track data, interception equipment track data and position area information data; based on target radar track data, extracting a first feature in an RCS form dimension, and extracting a second feature in a motion dimension; based on the target radar track data and the track data of the monitoring equipment, determining a frequency spectrum monitoring correlation factor and regional position information features; performing feature fusion on the first feature, the second feature, the spectrum interception correlation factor and the regional position information feature to obtain a target feature, and identifying a target type; and identifying a target threat level based on the target type and the target radar track data. The method and the device are used in a target identification process based on multi-source information fusion, and the technical problem that the target threat degree cannot be accurately identified in a complex environment in the prior art is solved.

The invention relates to the technical field of food fresh-keeping materials, and discloses a preparation method of an intelligent response degradable fruit and vegetable fresh-keeping film based on a Janus structure, the intelligent response degradable fruit and vegetable fresh-keeping film comprises a hydrophobic layer and a hydrophilic layer, the hydrophobic layer is an electrostatic spinning film formed by compounding eugenol-loaded ZIF-8 nano-particles and polylactic acid, and the hydrophilic layer is an electrostatic spinning film formed by compounding eugenol-loaded ZIF-8 nano-particles and polylactic acid. The hydrophilic layer is an electrostatic spinning membrane formed by compounding chitosan and gelatin, the surface of the hydrophobic layer is covered with the hydrophilic layer through an electrostatic spinning technology, the ZIF-8 nanoparticles are synthesized through a room-temperature stirring method, and synthesis raw materials are a 2-methylimidazole methanol solution and a zinc nitrate methanol solution. By means of the pH response characteristic of the ZIF-8 nano-particles, pH value change signals generated in the early stage of fruit and vegetable putrefaction can be sensitively captured, loaded antibacterial components are accurately released through ZIF-8 framework dissociation, targeted inhibition on putrefying bacteria is achieved, passive protection is converted into active intervention, the fruit and vegetable putrefaction process is effectively delayed, the fresh-keeping effect is improved, and the fresh-keeping time is prolonged.

The invention discloses a wind field detection method, device and equipment based on wind measurement data fusion and a medium, and relates to the technical field of wind measurement, and the method comprises the steps: carrying out the quality control of initial coherent wind measurement data and initial direct wind measurement data, and obtaining target coherent wind measurement data and target direct wind measurement data which meet preset quality requirements; performing space-time consistency matching on the target coherent wind measurement data and the target direct wind measurement data to obtain a target matching result, and splicing the target direct wind measurement data to the target coherent wind measurement data according to the target matching result to obtain spliced wind measurement data; performing data fusion on data in a to-be-fused height range in the spliced wind measurement data by using a real-time fitting prediction method to obtain fused wind measurement data, and determining a target wind field detection result by using the fused wind measurement data; the to-be-fused height range is a height range determined based on the maximum value and the minimum value of the height corresponding to the target coherent wind measurement data in the fusion time sliding window; therefore, the accuracy of wind measurement data can be improved.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention provides a question and answer processing method, device and equipment, a computer readable storage medium and a computer program product. The method comprises the steps of executing vector semantic retrieval in a vector database set of each target knowledge base to obtain a first candidate result set, and executing keyword retrieval in each target knowledge base to obtain a second candidate result set; fusing the first candidate result set and the second candidate result set to obtain single-library candidate result sets corresponding to the target knowledge bases, and merging all the single-library candidate result sets to obtain a global candidate result set; sorting the global candidate result set to obtain a global sorting result set; selecting a target number of candidate results from the global sorting result set to obtain a final sorting result set; and generating answers based on the final sorting result set by using a large language model. Through the method, the authenticity and reliability of the intelligent question-answering result can be improved, and a solid data support is provided for intelligent question-answering service.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a memory analysis question and answer data construction method and device, equipment and a medium. The method comprises the steps of extracting a fact information set containing entities, relationships and values based on structured message representation, generating candidate question and answer pairs by utilizing a language model, executing multi-source verification to form a target question and answer set, performing semantic extension and question method transformation to generate a diversified question and answer set, and completing credibility screening based on a verification result to obtain a memory analysis question and answer set. And generating a memory analysis data set in combination with the user message sequence. According to the method, through multi-stage extraction, generation, verification and screening, reliability and diversity unification of question and answer content are achieved, the constructed memory analysis data set has the advantages of being consistent in context, accurate in fact, rich in expression and the like, and therefore the credibility of memory ability evaluation of the large language model is improved.

The invention provides a hotspot technology mining method and system based on scientific and technological literature data, and relates to the technical field of scientific and technological information mining, and the method comprises the steps: firstly obtaining a scientific and technological literature data set containing papers and patent literatures, and constructing a current technical element network through technical phrase extraction and semantic normalization processing; dividing the technical element network segment into a time-serialized technical element network segment set according to time slices, calculating nodes and edge time sequence importance indexes to judge an emerging technical cluster, carrying out dynamic graph representation learning and future associated edge prediction on the set, generating a potential technical combination prediction set, and evaluating the potential technical combination prediction set; and finally, generating an emerging technology theme list and a technology fusion direction list in combination with an emerging technology cluster node growth trend and a prediction association edge evaluation result, thereby improving the accuracy, comprehensiveness and foresight of hotspot technology mining.

The invention relates to a BERT and multi-module fusion-based track circuit fault label classification method and device and a medium, and the method comprises the steps: obtaining a to-be-classified track circuit fault text, and carrying out the cleaning and standardization processing of the to-be-classified track circuit fault text; performing word segmentation and vectorization processing on the track circuit fault text to generate a lexical symbol sequence conforming to BERT encoder input specifications; extracting a context semantic vector representation of the input lexical symbol sequence through a BERT encoder; performing feature extraction through a TextCNN module and a self-attention module which are parallel to obtain local features and weighted global features, and splicing the local features and the weighted global features to obtain a comprehensive feature vector; and synchronously generating a multi-label classification result of a fault phenomenon, a fault reason and a solution measure corresponding to the current fault text through a multi-label classification head based on the comprehensive feature vector. Compared with the prior art, the method has the advantages of high discrimination, high accuracy, high practicability and the like.

The invention discloses a power grid monitoring alarm event handling method and system based on a large model, and relates to the technical field of power systems, and the method comprises the steps: obtaining a historical sample of a power grid monitoring alarm event, and carrying out the preprocessing; constructing a balance training sample set based on a first large language model and resampling combined sample enhancement method; based on the pre-training language model, utilizing the balance training sample set to carry out fine tuning training, and constructing a power grid monitoring alarm event diagnosis model; on the basis of a second large language model, a power grid monitoring alarm event disposal model is constructed, and the disposal model is used for retrieving reference information from a historical disposal knowledge base in combination with an event type diagnosis result output by the diagnosis model and generating an auxiliary disposal suggestion for an input alarm event text; and integrating the diagnosis model and the disposal model to form a power grid monitoring alarm event disposal framework. According to the invention, intelligent diagnosis and auxiliary disposal of the power grid monitoring alarm are realized, and the diagnosis accuracy and the disposal efficiency are improved.

The invention discloses a communication base station lithium battery fault diagnosis method and device and electronic equipment, relates to the technical field of lithium battery monitoring and fault diagnosis, and is used for solving the problems that the communication base station lithium battery fault diagnosis is inaccurate, and the fault source is difficult to locate. According to the invention, multi-dimensional data acquisition, including voltage, current, temperature and electrochemical impedance spectroscopy, is carried out through the battery management unit; extracting static and dynamic inconsistency characteristics based on the collected data, and calculating a comprehensive inconsistency index; similarity matching is carried out by combining the impedance deviation spectrum with a fault feature library, and the fault type and grade are identified; comprehensively judging a fault source through fault monomer space positioning, electrical topological correlation analysis, impedance spectrum deep analysis and thermal behavior anomaly analysis; and the accuracy of fault diagnosis and root positioning is obviously improved.

The invention discloses a hydroelectric generating set fault diagnosis data enhancement method based on a diffusion model and generative adversarial training, and belongs to the field of industrial equipment fault diagnosis. According to the method, the data enhancement model combining the diffusion model and the generative adversarial network is constructed, a multi-modal non-Gaussian distribution mechanism and a latent variable control generation process are introduced, the limitation of a single Gaussian hypothesis of a traditional diffusion model is broken through, and richer and more real sample generation is realized. And a condition discriminator and a time sequence modeling mechanism are introduced, so that the training of the model under different noise levels is more stable, and the authenticity judgment capability of the discriminator on the generated sample is enhanced. The problems that an existing hydroelectric generating set fault diagnosis system is insufficient in sample, low in generated sample quality and unreal in sample distribution are solved, and the diversity and quality of data are remarkably improved.

This invention provides a cup assembly for a food processor, including a cup body, a cup lid, and a processing component disposed within the cup body. It also includes a gearbox, which comprises a movably disposed clutch output shaft. When the clutch output shaft moves to a first position, it drives the processing component to output a first rotational speed; when the clutch output shaft moves to a second position, it drives the processing component to output a second rotational speed. The cup lid is installed in place, maintaining the clutch output shaft in either the first or second position. Furthermore, a food processor with the aforementioned cup assembly is provided. In the first position, the clutch output shaft outputs a first rotational speed to adapt to multi-functional cooking; in the second position, it outputs a second rotational speed to adapt to another multi-functional cooking method requiring a different speed. By changing the position of the clutch output shaft, the processing component can be replaced and the rotational speed can be automatically switched. Moreover, the output speed range is wide and the torque is high, avoiding stalling caused by high processing resistance.

The invention relates to a debugging method for an automatic system of an intelligent substation, and relates to the technical field of intelligent substations, and the method comprises the steps: analyzing a substation system configuration description file; obtaining physical network topology configuration information; mapping the virtual terminal connection relation to physical network topology configuration information; calculating a congestion delay parameter of each physical link based on the association table and a set fault scene; generating a test excitation sequence according to the to-be-tested protection logic, and distributing a transmission delay offset for a test message in the test excitation sequence according to the congestion delay parameter; sending a test excitation sequence carrying the transmission delay offset to the tested electronic equipment and monitoring an action message fed back by the tested electronic equipment; calculating a theoretical effective time window of the action message; and judging a debugging result according to whether the timestamp of the monitored action message falls into a theoretical effective time window or not. The method has the effect of improving the comprehensiveness of debugging of the automatic system of the intelligent substation.

The invention discloses an aero-engine vibration signal generation method under extreme imbalance, and belongs to the technical field of aero-engine fault signal generation. The method comprises the following steps: acquiring an original vibration signal at a target position of the aero-engine, and performing preprocessing operation; taking the preprocessed real vibration signal as a training sample, constructing a forward noise adding process, and training the improved one-dimensional diffusion generation network to learn a reverse denoising mapping relation; and inputting random Gaussian noise into the one-dimensional diffusion generation network after training convergence, and generating a target vibration signal matched with a real vibration signal feature through a reverse diffusion denoising process. Through targeted improvement of the diffusion generation network, the problem of data imbalance is effectively relieved, the model deployment cost is reduced, the signal generation capability of the model for minority types of faults is improved, and the method is suitable for fault signal generation scenes of aero-engines under complex working conditions.

The invention discloses a forest fire early recognition and early warning system and method based on multi-source sensing and artificial intelligence, and belongs to the technical field of forest fire intelligent monitoring. The system comprises a gridding multi-source sensing deployment module, a multi-modal time sequence data collaborative acquisition module, a cross-modal feature coupling and state diagnosis module, a multi-source heterogeneous feature adaptive fusion and fire behavior identification module and an intelligent grading early warning and dynamic resource collaborative scheduling module. The method comprises the steps of heterogeneous grid division and sensing deployment, multi-modal time series data collaborative acquisition, cross-modal feature coupling analysis, multi-channel attention fusion recognition, and graded early warning and dynamic scheduling. Through multi-source data collaborative sensing, cross-modal feature coupling analysis, adaptive fusion recognition and intelligent scheduling response, accurate and rapid intelligent early warning of the early stage of the forest fire is realized, and the intelligent level and emergency response efficiency of forest fire prevention and control are significantly improved.

The invention provides a digital quantum simulation method, which comprises the following steps of: performing symmetry analysis and subspace decomposition on a target Hamiltonian to obtain an effective Hamiltonian and a symmetry constraint condition; constructing a target unitary operator according to the effective Hamiltonian, and initializing a quantum circuit population of the target unitary operator; calculating entanglement characteristics of each quantum line in the quantum line population, performing structure variation on the quantum line population based on the entanglement characteristics, and performing line fragment crossing in combination with a symmetry constraint condition to obtain a candidate line set; performing line parameter optimization on the candidate line set based on the fidelity gradient to obtain an optimized line set, and extracting gate distribution structure features of the optimized line set; and non-uniform sampling is carried out on the optimized line set based on gate distribution structure characteristics to obtain a sampling line subset, an optimal quantum line is screened out from the sampling line subset based on the deviation between an experimental observation value and a theoretical expected value, and quantum simulation is carried out based on the optimal quantum line.

The invention discloses an English teaching writing error diagnosis method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: obtaining an English composition text, and obtaining initial writing data; performing grammatical structure analysis according to the initial writing data to obtain grammatical features; performing vocabulary error analysis according to the grammatical features to obtain vocabulary errors; performing syntactic and semantic consistency analysis according to the vocabulary errors to obtain syntactic and semantic errors; performing article logic analysis according to the syntactic and semantic errors to obtain article logic errors; and performing comprehensive diagnosis according to the article logic error to obtain a diagnosis result. According to the method, the conjunction matching degree and the semantic compatibility are combined, cross-sentence logic analysis is achieved, misjudgment of isolated sentences is avoided, a complete error set is formed, the comprehensiveness and accuracy of diagnosis are improved, and practicability and guidance are enhanced.

The invention discloses a wide-spectrum rapid scanning and intelligent identification method, system and device, and the method comprises the steps: firstly, carrying out the rapid frequency sweeping through employing a low-sampling-rate and low-quantization-precision receiver, generating a low-resolution time-frequency graph, and recovering the super-resolution of the low-resolution time-frequency graph into a high-resolution spectrum through a pre-trained deep learning model; then, the enhanced time-frequency image is segmented into time-frequency blocks, global features are extracted through a Transform encoder, and detection and screening of multiple candidate signal areas are achieved; and finally, carrying out directional sampling on the candidate region, respectively extracting time domain and frequency domain features, mapping the time domain and frequency domain features to a unified space for fusion, and completing signal category judgment based on the combined features and a judgment network. According to the wide-frequency-spectrum rapid scanning and intelligent identification method, system and device, through a layered architecture of broadband rapid scanning-frequency spectrum quality recovery-multi-region detection-directional sampling and time-frequency fusion identification, the reliability of unmanned aerial vehicle signal detection and identification in a complex electromagnetic environment is improved while the hardware cost is reduced.

The invention discloses a Linux-oriented vulnerability repair verification method and system and a medium. The method comprises the steps of creating a test application form for a CVE vulnerability patch in a task management system, preparing a test environment for CVE vulnerability repair verification as well as a required test table, a software source and a test strategy file, and calling a tool interface to automatically trigger an assembly line; the assembly line executes the test suite in the test environment based on the test table, the software source and the test strategy file and collects an execution state, and if the execution state is failure, automatic retry can be carried out on the CVE vulnerability based on manual triggering; and finally, generating a report file, writing a verification result for the test application form in the task management system, and if the verification result is that verification is passed, issuing a service pack of the CVE vulnerability. The invention aims to realize efficient, traceable and extensible Linux vulnerability repair verification, realize full automation and intelligence of the vulnerability verification process, and improve the accuracy, traceability and expandability of verification.

The invention discloses a shift change information intelligent complementation and error early warning system based on a large language model, which belongs to the technical field of medical information processing and comprises a terminal acquisition module, an intelligent distribution complementation module and an early warning control module. Structure and semantic fingerprints are generated for the data table, a shift exchange asset meta model is constructed through fusion similarity clustering merging, and a semantic enhancement model is generated in combination with medical shift exchange specifications to complete information field completion; the intelligent distribution and completion module configures an optimal path with the minimum comprehensive overhead for the acquisition module by relying on a regional dynamic shift information network formed by double sub-networks; the early warning control module is combined with a large language model semantic analysis and rule engine, monitors an acquisition track and identifies information abnormity to execute error early warning, and an early warning result is fed back to the intelligent distribution complementation module, so that dynamic adjustment of an optimal path is realized, and the integrity, accuracy and acquisition efficiency of medical shift information are improved.

The invention provides a chemical reaction diagram examination method, device and equipment, and the method comprises the steps: carrying out the information extraction of a chemical reaction diagram, and obtaining the information of a plurality of semantic units in the chemical reaction diagram; determining an association relationship among the multiple pieces of semantic unit information; according to the semantic unit information and the association relationship, the chemical reaction diagram is examined, so that the examination accuracy and efficiency of the chemical reaction diagram can be improved.

The invention provides a data analysis system and method for evaluating a data center cold water link state, and belongs to the technical field of data center infrastructure intelligent operation and maintenance. According to the system, multi-source operation data of a cold machine, a water pump, a tail end air conditioner and a cabinet monitoring system in a cold water link are collected, and a dynamic threshold pool is constructed after working condition layering preprocessing so as to identify equipment abnormity; the method comprises the following steps: establishing a directed graph model based on a chilled water system topological structure, constructing a Bayesian network by fusing process logic, and reversely calculating a root cause posterior probability of each device when a cabinet hotspot event occurs; and generating an influence weight ranking list by integrating the abnormal strength of the equipment, the root cause probability, the topological key coefficient and the redundancy weakening coefficient, and further performing quantitative scoring and state grading on the overall availability of the cold water link. According to the method, working condition self-adaptive anomaly detection, robust root cause positioning and executable operation and maintenance priority ranking of the cold water link are realized, and the operation and maintenance efficiency and reliability of the refrigeration system of the data center are improved.

The invention relates to an unmanned aerial vehicle path planning method and device, electronic equipment and a readable storage medium. The method comprises the steps that environment information of a task area and constraint parameters of an unmanned aerial vehicle are acquired; initializing a population of the improved beaver optimization algorithm; determining a fitness value for each individual in the population based on a preset cost function; based on the fitness value, dynamically allocating a role to each individual in the population, updating the position of the individual by adopting a corresponding updating strategy according to the allocated role, and executing information exchange between roles in the updating process; and based on a preset termination condition, determining an individual with the lowest fitness value from the population after iteration updating, and outputting a path point sequence represented by the individual as a planned unmanned aerial vehicle flight path. Through the method provided by the invention, the local optimal escape ability and the quality of the solution are enhanced, the flight safety and the terrain adaptability are improved, and the overall performance and the reliability of unmanned aerial vehicle path planning are improved.

Embodiments of the present application disclose a resource recall method, a computer device and a storage medium. The method comprises: obtaining a seed resource generated by a target object in a preset time period and a topic information of the seed resource based on an interaction behavior; based on the seed resource and a heterogeneous graph, finding a first resource with a similarity higher than a similarity threshold to the seed resource; wherein the heterogeneous graph comprises a plurality of nodes, the plurality of nodes comprise topic nodes and resource nodes, and a weight of an edge between each two nodes in the plurality of nodes is used to indicate an association degree between the each two nodes; based on the topic information and the heterogeneous graph, finding a second resource associated with the topic information; and taking the first resource and the second resource as recall resources of the target object. The embodiments of the present application can accurately find resources interested by an object based on a topic interested by the object, thereby improving the accuracy of resource recall.

This utility model relates to a multi-segment adhesive cutting device, including a frame, a cutting component for cutting the outer layer of copper busbars or cables, a clamping component for holding the copper busbars or cables, a moving component for controlling the movement of the cutting component, and a bracket for supporting the copper busbars or cables. The cutting component is connected to the frame via the moving component, and the bracket is connected to the frame. The moving component is disposed within the frame. The cutting component includes a cutting fixed frame, an upper cutting unit, a lower cutting unit, and a transverse cutting unit. The cutting fixed frame and the moving component are fixedly connected. The upper cutting unit is disposed at the upper end of the cutting fixed frame, the lower cutting unit is disposed at the lower end of the cutting fixed frame, and the transverse cutting unit is disposed at the front end of the cutting fixed frame. The design of this utility model can not only achieve circumferential cutting of copper busbars or cables, but also achieve transverse cutting of copper busbars or cables, and is not limited to stripping the outer layers at both ends of the copper busbars or cables, thus improving the versatility of the device in use.

The utility model relates to the technical field of building engineering monitoring, and discloses a foundation pit engineering monitor for a high-rise building, which comprises a column body, and detection cavities arranged at equal intervals are formed in the column body. According to the foundation pit engineering monitor for the high-rise building, real-time monitoring of different positions and depths of a foundation pit can be realized through the action between the detection cavity and the detection rod, so that the stability and safety of the foundation pit can be known more comprehensively; and under the cooperative action of the pressure sensor, the mounting plate, the displacement sensor, the tension spring and the abutting plate, monitoring data are enriched, workers are helped to better master specific conditions in the foundation pit, and therefore multi-point and multi-dimensional real-time monitoring can be achieved, the comprehensiveness and accuracy of the monitoring data are improved, and the working efficiency is improved. And then under the action of the horizontal adjusting assembly, it can be ensured that the column body is horizontal in the deep foundation pit, so that it is ensured that the column body can be kept in the horizontal state in the installation process, and the accuracy of monitoring data is improved.