75results about How to "Improve regulatory efficiency" patented technology

The invention discloses an intelligent parking building vehicle dynamic supervision method and system based on an AI intelligent engine, and the method comprises the steps: obtaining original data from a plurality of sensors, carrying out the time alignment, standardization and noise reduction processing, and extracting initial features; performing reliability evaluation on the initial features, generating a dynamic weight matrix, and performing feature fusion; constructing a vehicle trajectory model and extracting a key point sequence, and performing space-time correlation analysis; constructing an exception mode library based on the optimized incidence matrix and a historical exception database, and performing exception matching; and constructing a decision model according to the abnormal event report and the system state matrix, and generating an execution strategy and an instruction sequence. According to the method, the multi-source data fusion and depth feature extraction technologies are adopted, and tensor decomposition and multi-objective optimization methods are combined, so that efficient and accurate supervision of the vehicles in the parking building is realized, and the method has relatively high practicability.

The invention relates to the technical field of leakage traceability, in particular to a water transfer project leakage traceability identification method and system, and the method comprises the following steps: setting monitoring points in a water transfer project, collecting the temperature, pressure and flow velocity data of the key monitoring points, carrying out the time sequence analysis of parameters, analyzing the water flow dynamic state and environment change in real time, and generating state monitoring data. According to the method, the environment change near the leakage is analyzed by using the state monitoring data, the abnormal change is identified through simulation comparison analysis, the leakage point is predicted and positioned more accurately, the energy state before and after the leakage is evaluated through entropy increase calculation, and the leakage position and time are further predicted accurately; by simulating energy flow under differentiated environment conditions, the accuracy of seepage path identification is improved, and more efficient seepage source positioning is realized, so that the whole seepage traceability identification process is more systematic and scientific, and the safety supervision and maintenance efficiency of water conservancy facilities is greatly enhanced.

The invention belongs to the technical field of environmental health risk identification and supervision, and discloses an environmental health risk assessment method and system for combined pollution of a metal smelting area, and the method comprises the following steps: S1, constructing an environmental health risk assessment system; s2, acquiring and inputting data; s3, heavy metal environmental health hazard identification; s4, evaluating receptors of heavy metal environmental health; s5, heavy metal environmental health hazard assessment; s6, evaluating the exposure of the heavy metal environment health; s7, comprehensive evaluation of heavy metal environment health; and S8, judging the supervision priority. Aiming at various heavy metal environmental health risks under multiple exposure media in the production activity of the metal smelting area, based on multiple steps, multiple models and graded evaluation, the method considers comprehensiveness and differentiation of evaluation results, determines a preferential supervision area, carries out targeted differential supervision, and improves the safety of the production activity of the metal smelting area. The efficiency of identifying and supervising the environmental health risk of the metal smelting area is improved.

The invention relates to a system and method for assisting in judging whether salary debt behaviors exist or not, and belongs to the technical field of computer network communication. The system comprises a management server, a data acquisition module, a data analysis module, an early warning prompt module, a complaint processing module and a credit evaluation module; the data acquisition module is in bidirectional signal connection with the management server, the data analysis module is in bidirectional signal connection with the management server, the early warning prompt module is in bidirectional signal connection with the management server, and the complaint processing module is in bidirectional signal connection with the management server. According to the system and the method for assisting in judging whether the salary debt behavior of the migrant worker exists, through mutual cooperation among the management server, the data acquisition module, the data analysis module, the early warning prompt module, the complaint processing module and the credit evaluation module, key data such as fund flow and salary payment conditions of an engineering project can be monitored in real time; and early warning is immediately carried out once abnormity is found, so that the timeliness and accuracy of supervision are greatly improved.

The invention belongs to the technical field of informatization security and secrecy, and particularly relates to an encrypted optical disc illegal external connection alarm tool which comprises a recording and printing tool recorded in an optical disc in an encrypted mode and an internet detection module. And automatically associating a specific website after the recording and printing tool is opened, and judging that the compact disc is used illegally if the association is successful. According to the invention, information security management can be enhanced, the management efficiency is improved, the information security cost is reduced, comprehensive monitoring and timely alarm of the secret-related optical disk are realized, and the security and confidentiality of the secret-related information are greatly improved.

The present invention relates to a method and system for regionalized environmental supervision of aquaculture based on data analysis. The method comprises: obtaining a non-aquaculture period in an aquaculture area, obtaining the aquatic plant proliferation rate and the average growth time of the aquatic plants according to the non-aquaculture period; screening out nutrient areas of different levels from the aquaculture area according to the aquatic plant proliferation rate and the average growth time of the aquatic plants; continuously monitoring and analyzing nutrient areas of different levels, and determining whether the aquaculture area is in a trend of trophic imbalance according to the monitoring and analysis results; if it is a trend of trophic imbalance, analyzing the placement of materials in the aquaculture area, and regulating the environment of the aquaculture area according to the analysis results. The present invention performs water quality trend analysis on the aquaculture area, improves the efficiency of environmental supervision, ensures that the environment of the aquaculture area meets the actual breeding needs, increases the aquaculture output, and can control the aquatic management and control costs of the aquaculture area.

The invention belongs to the technical field of surface water monitoring and environment management, and particularly relates to a regional water environment quality intelligent supervision platform comprising a monitoring data acquisition and anti-interference module used for acquiring data information related to a water environment, the module comprising an electronic fence setting and monitoring unit, a double-layer yellow fence is arranged at a surface water national test section water collecting port, the inner layer prevents man-made interference monitoring data, the outer layer monitors displacement of water collecting facilities, and the surrounding environment is managed in combination with video monitoring, positioning and electronic map technologies; the artificial intelligence identification and alarm module is responsible for identifying abnormal conditions occurring in the monitoring process and sending out alarm information; according to the invention, by constructing the intelligent supervision system comprising a data acquisition module, an intelligent identification module, a three-dimensional supervision module and the like, the problems of much man-made interference, non-visual data application, difficulty in tracing and the like in surface water national examination section monitoring are solved, and accurate and efficient supervision is realized.

The invention discloses a non-intrusive electronic scale supervision device and a working method thereof, and the device comprises a data collection module which is used for obtaining the weight, unit price and total amount data of an electronic scale, and the operation data of a user on a matrix keyboard of the electronic scale; the tamper-proof detection module is used for monitoring data transmission states of a nixie tube display tube, a matrix keyboard and a power supply of the electronic scale and a main control chip of the electronic scale so as to record dismounting behaviors; the data processing module is used for receiving data of the data acquisition module and the tamper-proof detection module and integrating the data into a data packet; the data storage module is used for storing the data packet according to the timestamp; the communication module is connected with the management platform and used for the management platform to retrieve the data packet. By implementing the device provided by the invention, the working data of the electronic scale can be recorded, tamper-proof monitoring is carried out from software and hardware levels, and the supervision efficiency of the electronic scale is improved.

The invention relates to a block chain and proxy re-encryption-based outbound data sampling inspection supervision method. The method comprises the steps of generating a first public and private key pair of a data outbound enterprise and a second public and private key pair of a supervisor according to public parameters; encrypting the plaintext data by using the first public and private key pair; carrying out sampling inspection on the first ciphertext obtained by encryption to obtain a second ciphertext, and sending sampling inspection information obtained according to the second ciphertext to the block chain node; after the re-encryption key passes verification, the block chain node re-encrypts the second ciphertext by using the generated re-encryption key; decrypting the obtained third ciphertext to obtain plaintext data; and checking the plaintext data by using the declaration record, and uploading an obtained spot check result to the block chain node. According to the method, the risk of large-scale privacy disclosure is avoided, the supervision efficiency and the supervision effectiveness and reliability are improved, spot check information can be provided as evidence, the transparency of a supervision process is improved, and the trust between an enterprise and a supervision department is enhanced.

The invention provides an AI analysis-based visual union monitoring system and method for a household garbage classification source, and relates to the technical field of garbage classification monitoring. A preset monitoring area is obtained, in-can monitoring and out-of-can monitoring are carried out, acquired data are obtained, data fusion is carried out, and area fusion acquisition information is obtained; obtaining and fusing abnormal data outside a region barrel and abnormal data inside the region barrel of the preset monitoring region, calculating a region anomaly classification coefficient, and carrying out depth anomaly classification judgment to obtain region anomaly classification judgment information; according to the method, classification monitoring and comprehensive monitoring of the inside and outside of the barrel can be carried out on the preset monitoring area, and then comprehensive classification management of the area monitoring network is achieved; and the efficiency and the accuracy of garbage classification linkage monitoring are improved.

The invention particularly relates to an online commodity bogus transaction risk early warning method and system. The online commodity bogus transaction risk early warning method comprises the following steps: acquiring public commodity information of a network retail platform and e-commerce platform list data in the same time period, and storing the public commodity information and the e-commerce platform list data in a database; standardization processing is carried out, a commodity table and a list commodity table are established, and the monthly sales volume of a commodity list, the lowest monthly sales volume of the list under the same platform category and the highest monthly sales volume of the list under the same-time-period standard category are obtained; judging whether the commodity has a false sales volume risk or not, and performing risk early warning according to a pre-established judgment standard; according to the online commodity bogus transaction risk early-warning method and system, illegal behaviors such as bogus transactions and virtual bid sales volume can be identified in batches, the supervision time cost is reduced, the supervision efficiency is improved, correct reference can be provided for purchase of consumers, compliance guidance can be provided for operators, and data support and factual basis can be provided for market supervision and data analysis.

The invention relates to the technical field of network flow optimization, in particular to an intelligent analysis and optimization method based on network flow. The method comprises the following steps: acquiring a network flow log in a management time period, acquiring an actual current-carrying value and a preset current-carrying value of network equipment based on the network flow log, and marking a ratio of the actual current-carrying value to the preset current-carrying value as a network flow index WL in the management time period; judging whether the operation situation of the network equipment in the management time period meets an anomaly analysis requirement or not through the network flow index WL; under the condition that the operation situation of the network equipment meets the exception analysis requirement, obtaining a network equipment operation situation index in the management time period, and obtaining a network equipment operation risk index based on the network equipment operation situation index; and judging whether to activate a network flow optimization model based on the network equipment operation risk index, and if yes, activating the network flow optimization model and optimizing the network flow in the management time period. According to the invention, the optimization efficiency of the network flow can be improved.

The present invention relates to a sulfur hexafluoride monitoring system and a carbon neutrality data processing method based on artificial intelligence, and belongs to the field of power grid artificial intelligence technology. The system includes a sulfur hexafluoride gas cylinder, a controller, a sulfur hexafluoride density monitor, a secondary sulfur hexafluoride gas cylinder, an air pump, and a recovery gas cylinder; the sulfur hexafluoride gas cylinder and the controller are connected through a main pipeline; a main valve is provided between the sulfur hexafluoride gas cylinder and the main pipeline; a control valve is provided between the controller and the main pipeline; the controller and the secondary sulfur hexafluoride gas cylinder are connected through a secondary pipeline; a secondary valve is provided between the secondary sulfur hexafluoride gas cylinder and the secondary pipeline; the sulfur hexafluoride density monitor is connected to the main pipeline through a debugging pipeline. It solves the problem of lack of supervision of sulfur hexafluoride gas emissions during the maintenance, construction or debugging of sulfur hexafluoride equipment in GIS equipment substations, and promotes the improvement of greenhouse gas emission reduction and carbon neutrality monitoring levels of power grid companies.

The application discloses a data collection method and device, equipment and a computer storage medium. The method comprises the following steps: in the case that a target field is determined from a field mapping relationship, acquiring an original data file based on the target field according to a collection mode and a collection frequency of the original data file, generating a data information file according to a predetermined rule based on the original data file, encrypting the original data file based on a predetermined encryption rule to obtain an encrypted file, and sending the data information file and the encrypted file to a server according to a predetermined path, so that the server decrypts the encrypted file, generates a reconciliation file according to a predetermined rule in the case that a decrypted file is obtained, and completes data collection in the case that the data information file and the reconciliation file are consistent. According to the embodiment of the application, local financial institution supervision data can be collected in time, accurately and efficiently, and the supervision efficiency is improved.

The invention discloses a device for automatically detecting drunk driving and connecting a law enforcement system to automatically give an alarm, and relates to the technical field of traffic safety, the device comprises a shell, a gas detection device, a normal light, an alarm light, a two-dimensional code and a camera, the shell is of a hard rectangular body structure; the gas detection device is embedded in the front surface of the shell, and the main body of the gas detection device is an alcohol detector; the normal lamp is embedded in the front face of the shell and located above the gas detection device, and when the alcohol concentration in the gas does not reach the alarm standard, the normal lamp is turned on, and the detection result is qualified. The alarm lamp is embedded in the front face of the shell and located on one side of the normal lamp, and when the alcohol concentration in the gas reaches the alarm standard, the alarm lamp is turned on, and the detection result is unqualified. Compared with the prior art, the method has the advantages that the accuracy is high; instant alarm is realized; monitoring escape; the installation is convenient; and intelligent management is realized.

The invention relates to an intelligent management internet-of-things system and method for a traditional Chinese medicinal material GAP planting base. The problem that a planting management system in the prior art is difficult to monitor the growth environment of traditional Chinese medicinal materials in real time and actively improve and control is solved. The system comprises a data monitoring subsystem and an internet-of-things control subsystem, the data monitoring subsystem comprises an environment sensing assembly, the environment sensing assembly is connected with a data monitoring platform and stores monitoring data through a data storage center, the data storage center is connected with a data sampling unit, and the data sampling unit performs data sampling and establishes an intelligent management model; the internet-of-things control subsystem comprises a control center, and the control center is connected with the planting supply mechanism and the environment adjusting mechanism. The system has the advantages that the growth environment of the traditional Chinese medicinal materials is monitored in the whole process, the growth environment of the traditional Chinese medicinal materials is actively adjusted through the control center, it is ensured that the traditional Chinese medicinal materials are in the optimal growth environment, and the quality and yield of the traditional Chinese medicinal materials are improved through accurate planting management and environment control.

The invention belongs to the technical field of environment monitoring, and discloses an aquaculture environment monitoring method and device. The method comprises the following steps: acquiring original water body data acquired by a water quality sensor; carrying out abnormal value detection on the original water body data; processing the original water body data based on the detection result to obtain processed water body data; constructing a BP neural network, and optimizing a connection weight and a threshold value of the BP neural network by using a PSO algorithm to train the sample water body data to obtain a water body environment monitoring model; and inputting the processed water body data into the water body environment monitoring model to obtain a monitoring result output by the water body environment monitoring model, combining the PSO algorithm with the BP neural network to construct the water body environment monitoring model in the above mode, and through scientific data comparison and analysis, the accuracy of monitoring is ensured without relying on subjective experience judgment of aquaculture personnel, and the accuracy of monitoring is improved. And meanwhile, the aquaculture supervision efficiency is also improved.

The invention discloses a remote supervision system for motor vehicle detection, and relates to the technical field of motor vehicle detection, and the system comprises a detection data collection module which employs a multi-type sensor array to collect tail gas emission, vehicle speed, braking, light and axle load data at a detection line, and transmits the data to a data collection unit for caching through a high-speed wired network; the data encryption transmission module is used for encrypting data through a national cryptographic algorithm, transmitting the data to a remote supervision center through a wireless network or a VPN network, and authenticating the data through a digital certificate during transmission; the remote supervision center module is used for processing, analyzing and verifying data by a high-performance server cluster and storing the data in a distributed database; and the intelligent analysis and early warning module analyzes the data by using big data and an artificial intelligence algorithm, and pushes early warning in multiple modes when abnormity occurs. According to the invention, the supervision efficiency and precision are improved, multiple types of sensors and an intelligent identification technology ensure accurate and standard detection, guarantee data security and reliable encryption transmission and distributed storage, and can early warn risks in time and optimize the management of a detection mechanism.

The invention discloses an intelligent supervision method and system based on medical treatment and public health, and relates to the technical field of medical supervision. A target object needing medical treatment and public health supervision is selected, full-process medical data related to medical treatment and public health of the target object are collected, different medical data carriers of the full-process medical data are identified, and the target object is subjected to medical treatment and public health supervision; each medical data carrier controls acquisition of medical data of a respective corresponding stage in the whole-process medical data, data mining of the whole-process medical data is carried out to obtain medical data features in each medical stage, and standard data features of the medical data features in each medical stage are associated to obtain standard data features of the medical data features in each medical stage; the method comprises the steps of performing feature analysis on a to-be-analyzed medical data feature group of each medical stage to obtain a medical and health problem of each medical stage, generating a corresponding risk early warning prompt, processing the corresponding medical and health problem after receiving the risk early warning prompt, and performing feature analysis on the to-be-analyzed medical data feature group of each medical stage. And supervising execution objects for processing the medical and health problems, and evaluating the processing efficiency of the execution objects.

The invention discloses a cross-platform network content intelligent supervision method and system based on multi-modal AI, relates to the technical field of network content supervision, realizes unified acquisition and management of cross-platform content by establishing a multi-platform data acquisition adapter, and solves the technical problem that an existing system cannot realize cross-platform supervision. Secondly, deep fusion analysis of texts, images and videos is realized by constructing a multi-modal AI analysis engine, and the accuracy of content understanding and risk identification is greatly improved; accurate risk classification is realized by establishing a grading risk label system, and a scientific basis is provided for supervision decision making; and finally, by generating an intelligent supervision report and establishing a real-time monitoring early warning mechanism, a comprehensive and visual supervision analysis tool is provided for supervisors, and the supervision efficiency and effect are remarkably improved. Meanwhile, by adopting a ReAct reasoning framework, the system can perform multi-step reasoning analysis and perform comprehensive judgment in combination with analysis results of texts, images and videos, so that the limitation of single-modal analysis is avoided, and the accuracy and reliability of risk identification are improved.

In order to solve the problem of whether the construction worker wears a reflective vest in the prior art, the application provides a real-time high-precision reflective vest wearing state recognition method and device. The method first acquires a construction site image, then inputs the construction site image into a target detection network structure model, and finally obtains a target detection prediction result image of the construction worker and the reflective vest category. Whether the construction worker wears the reflective vest is determined by calculating the coincidence degree of the anchor frame of the reflective vest and the construction worker, so as to realize the identification of whether the construction worker of high-altitude operation or heavy equipment wears the reflective vest. The application is not only suitable for reflective vest detection in multiple scenes, but also has strong robustness. In addition, it can be applied to low-cost and lightweight operation equipment in the construction site with small parameter operation amount, so as to promote the realization of the intelligent construction site.

The utility model belongs to the technical field of safety valve calibration detection, specifically relates to a kind of safety valve digitalization supervision system, including data acquisition unit, electronic identification unit, hand-held terminal unit and data processing supervision unit, data acquisition unit includes pressure sensor, inspection data acquisition platform, data collector and calibration platform controller, electronic identification unit includes explosion-proof fixture and RFID radio frequency tag, RFID radio frequency tag is fixed on safety valve ontology by explosion-proof fixture, hand-held terminal unit includes hand-held PDA, and hand-held PDA is connected with RFID radio frequency tag by radio frequency signal, and data processing supervision unit includes cloud server and system terminal, and cloud server is connected with electronic identification unit, hand-held terminal unit and data acquisition unit respectively by wireless network.The utility model can realize the management supervision of safety valve full life cycle.

Disclosed in the present application are an energy storage device and a temperature regulating structure thereof. The temperature regulating structure of the energy storage device comprises: a housing, and a temperature regulating plate fixedly connected to the housing, wherein the housing and the temperature regulating plate form a first heat insulation cavity, and / or the housing is provided with a second heat insulation cavity. In the temperature regulating structure of the energy storage device, the first heat insulation cavity is formed by the housing and the temperature regulating plate and / or the housing is provided with the second heat insulation cavity. Use of the first heat insulation cavity and the second heat insulation cavity can reduce the heat exchange of the temperature regulating plate with the outside by means of the housing, thereby reducing the impact of the external environment on the temperature regulating plate and improving the regulation efficiency of the temperature regulating plate.

The present application provides a method, device, equipment and storage medium for detecting occlusion behavior; the detection method includes obtaining a target video and a target address corresponding to the target video; generating a target image set based on the target video; generating a target pixel point set corresponding to each of the target images based on the target image set; determining the occlusion pixel point ratio of each of the target images based on the target pixel point set; when the occlusion pixel point ratio of the target image exceeds a first preset range, setting the target image as an occlusion image; when the number of occlusion images exceeds a second preset range, setting the target video as an abnormal video with occlusion behavior, and setting the target address as an abnormal address. The present application can automatically identify whether there is an illegal behavior of blocking the camera in the back kitchen of a catering enterprise, greatly reducing the workload of supervisors and improving the efficiency of supervisors' supervision of catering enterprises.

The present invention discloses a system for identifying complex pollutants in industrial agglomerations based on big data, and relates to the technical field of industrial pollutants. The system comprises an acquisition module, a processing module and an output module, wherein the acquisition module stores a basic information data set of an industrial park, a chemical knowledge information data set, a factory emission information data set and an emission index information data set. The efficient collaboration of the acquisition module, the processing module and the output module of the present invention realizes the comprehensive monitoring and accurate identification of complex pollutants in industrial agglomeration areas. The acquisition module integrates multi-source data and provides a rich basis for analysis. The predictive analysis model in the processing module uses a long short-term memory network to extract key features, and through continuous optimization and updating, ensures the accuracy and timeliness of the model, can accurately identify the conditions for the generation of complex pollutants, predict their development trends, and provide timely feedback on factory information with excessive emissions.

This invention discloses a method and system for risk assessment and graded early warning of offshore wind farm vessels. The method involves collecting and fusing multi-source sensing data; performing spatiotemporal alignment and fusion processing on the fused multi-source sensing data to generate a bird's-eye view feature representation containing spatiotemporal characteristic information of the vessel; based on the bird's-eye view feature representation, employing a fusion prediction model combining a vessel motion physics model and a time series prediction algorithm, calculating the motion reachability set of the target vessel within the predicted time period; conducting a risk assessment based on the spatial relationship between the motion reachability set and a preset safe area of ​​the wind farm, and triggering graded early warnings based on the assessment results. This invention solves the problems of blind spots in single-source monitoring and high false alarm rates in traditional early warning systems, achieving accurate and dynamic assessment and proactive early warning of vessel collision risks in offshore wind farm waters, significantly improving regulatory safety and efficiency.

The invention relates to the technical field of medical informatics, in particular to an internal medicine chronic disease medication compliance safety monitoring system fused with a block chain, and is used for solving the problems that an existing system is lack of medicine traceability, poor in data safety, inaccurate in compliance judgment and insufficient in cross-mechanism collaboration. The system comprises a multi-modal intelligent medication terminal, an edge-federal cooperative processing node, a block chain main and side chain cluster, a fusion type dynamic evaluation platform and a self-adaptive hierarchical intervention engine, and each module encrypts link interaction through a national secret SM9 algorithm. The terminal captures the medicine taking action in a non-contact mode, collects physiological indexes and verifies the legality of the medicine in a double-code mode; the edge-federated node preprocesses data and performs federated learning to protect privacy; the main chain and the side chain of the block chain store traceability core data and real-time information; the evaluation platform outputs a four-level compliance result; the intervention engine dynamically adjusts the policy. The system realizes drug and data dual safety, improves the monitoring accuracy and intervention effect, adapts to multiple scenes and special groups, and assists cross-mechanism collaborative management and control.

The present invention discloses a non-tax special account supervision system and method based on electronic accounting vouchers, which relates to the field of data security supervision technology, including a supervision center, wherein the supervision center is communicatively connected with a data integration module, a data storage module, a data integration module, an anomaly detection module, an anomaly handling response module and a report analysis module, wherein the modules are connected by electrical signals. The present invention significantly improves the supervision efficiency through automated data integration and anomaly detection mechanisms, can monitor and analyze a large amount of electronic accounting voucher data in real time, and quickly identify abnormal situations. Compared with manual audit methods, the automated process reduces the possibility of human errors and omissions, and also reduces the work pressure of supervisors. In addition, a built-in risk warning mechanism immediately triggers the warning process once an abnormality or potential risk is found, which helps to take measures quickly to prevent the risk from expanding and spreading.

The application discloses an intelligent supervision system and method for electromechanical equipment based on artificial intelligence, relates to the technical field of electromechanical equipment supervision, and comprises an attribute combination set generation module, a key matching rule output module, a necessary optimization index analysis module, a comprehensive service influence degree analysis module, a rule updating module and a secondary verification response module; the attribute combination set generation module is used for generating an equipment attribute combination set; the key matching rule output module is used for extracting a key matching rule of an electromechanical equipment monitoring event record; the necessary optimization index analysis module is used for matching a necessary optimization index of a rule; the comprehensive service influence degree analysis module is used for analyzing a comprehensive service influence degree of a gray scale rule in which equipment attributes of a to-be-analyzed equipment group are located; the rule updating module is used for updating a key matching rule of the to-be-analyzed equipment group under a corresponding attribute combination; and the secondary verification response module is used for performing secondary verification on a target electromechanical equipment group in which the key matching rule is replaced.

The invention relates to the technical field of food safety, in particular to an edible forest product quality safety early warning method which comprises the following steps: starting an early warning system, initializing a model and acquiring a data set, collecting the data set including soil physical and chemical data, soil environment data and edible forest product data, analyzing the data, training a safety early warning model according to the data, and performing early warning; whether the spatial data of the sample to be predicted exceeds a set threshold value or not is judged, safety early warning is conducted, and data sets related to edible forest product quality safety are collected in real time and comprise soil physical and chemical data, soil environment data and edible forest product data, so that the real-time performance and accuracy of the data are ensured. The method provides a basis for timely discovery and early warning of potential quality and safety hazards, combines a geographic information system (GIS) technology, carries out spatial analysis and visual expression, can more visually recognize possible quality and safety hazards, and provides decision support for early warning and supervision.