Warehouse quality safety level evaluation method and system

Abstract

The invention provides a warehouse quality safety level assessment method and system, and relates to the technical field of quality assessment, and the method comprises the steps: connecting an intelligent warehouse control system, obtaining an assessment target, obtaining a safety risk index of a target warehouse, collecting the safety information of the target warehouse, dividing the target warehouse into p regions, setting a plurality of weights for different areas, and constructing a risk assessment model; and performing risk assessment on the p regions through a risk assessment model to generate q assessment results, and generating a total assessment result of the target warehouse by using a linear weighting model and the q assessment results. The problems that an existing method is low in efficiency, the condition change of warehouse quality cannot be reflected, and the evaluation result is inaccurate and incomplete are mainly solved. By constructing the risk assessment model to carry out partition assessment on the warehouse, an effective quality security level assessment result is provided, and the assessment efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of quality assessment, and in particular to a warehouse quality and safety level assessment method and system. Background Art

[0002] With the development of society and the improvement of safety awareness, people are paying more and more attention to warehouse safety issues. As an important place for material storage, the safety of warehouses is directly related to the production and operation of enterprises. Therefore, it is particularly important to conduct scientific and effective quality and safety level assessment of warehouses. Although there are some warehouse quality and safety level assessment methods, they often have some problems, such as the assessment indicators are not comprehensive enough, the assessment process is not objective enough, and the assessment results are not accurate enough. Therefore, a more scientific and effective assessment method is needed to improve the accuracy and objectivity of warehouse quality and safety level assessment.

[0003] However, in the process of implementing the technical solution of the invention in the embodiment of the present application, it is found that the above technology has at least the following technical problems:

[0004] The existing methods are inefficient, cannot reflect changes in warehouse quality, and have inaccurate and incomplete evaluation results. Summary of the invention

[0005] This application mainly solves the problems that the existing methods are low in efficiency, cannot reflect changes in warehouse quality, and have inaccurate and incomplete evaluation results.

[0006] In view of the above problems, the present application provides a warehouse quality and safety level assessment method and system. In the first aspect, the present application provides a warehouse quality and safety level assessment method, the method comprising: connecting to an intelligent warehouse control system, obtaining an assessment target, and obtaining a safety risk index of a target warehouse through the assessment target; collecting safety information of the target warehouse, the safety information including a warehouse floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical safety incidents; dividing the target warehouse into p areas, setting multiple weights for different areas, wherein p is a positive integer; constructing a risk assessment model through the safety risk index of the target warehouse and the safety information of the target warehouse; performing risk assessment on the p areas through the risk assessment model, and generating q assessment results, wherein q is a positive integer greater than or equal to p; and generating a total assessment result of the target warehouse using a linear weighted model and the q assessment results.

[0007] In a second aspect, the present application provides a warehouse quality and safety level assessment system, the system comprising: a safety risk indicator acquisition module, the safety risk indicator acquisition module is used to connect to an intelligent warehouse control system, obtain an assessment target, and obtain the safety risk indicator of a target warehouse through the assessment target; a safety information collection module, the safety information collection module is used to collect safety information of the target warehouse, the safety information including the warehouse's floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical safety events; an area division module, the area division module is used to divide the target warehouse into p areas, and set multiple weights for different areas, wherein p is a positive integer; a risk assessment model construction module, the risk assessment model construction module is used to construct a risk assessment model through the safety risk indicators of the target warehouse and the safety information of the target warehouse; a risk assessment module, the risk assessment module is used to perform risk assessment on the p areas through the risk assessment model, and generate q assessment results, wherein q is a positive integer greater than or equal to p; a total assessment result generation module, the total assessment result generation module is used to generate a total assessment result of the target warehouse using a linear weighted model and the q assessment results.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The present application provides a warehouse quality and safety level assessment method and system, which relates to the field of quality assessment technology. The method includes: connecting an intelligent warehouse control system, obtaining an assessment target, obtaining a safety risk index of a target warehouse, collecting safety information of the target warehouse, dividing the target warehouse into p areas, setting multiple weights for different areas, and constructing a risk assessment model; performing risk assessment on the p areas through the risk assessment model, generating q assessment results, and generating a total assessment result of the target warehouse using a linear weighted model and the q assessment results.

[0010] This application mainly solves the problems of low efficiency of existing methods, inability to reflect changes in warehouse quality, and inaccurate and incomplete evaluation results. By constructing a risk assessment model to conduct a zoning assessment of the warehouse, it provides effective quality and safety level assessment results and improves the evaluation efficiency.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flowchart of a warehouse quality and safety level assessment method is provided for an embodiment of the present application;

[0014] Figure 2 A schematic flow chart of a method for obtaining a safety risk index of a target warehouse in a warehouse quality safety level assessment method is provided for an embodiment of the present application;

[0015] Figure 3 A schematic flow chart of a method for generating abnormal fire risk in a warehouse quality and safety level assessment method is provided for an embodiment of the present application;

[0016] Figure 4 A structural schematic diagram of a warehouse quality and safety level assessment system is provided for an embodiment of the present application.

[0017] Explanation of the reference numerals: security risk indicator acquisition module 10 , security information collection module 20 , area division module 30 , risk assessment model construction module 40 , risk assessment module 50 , overall assessment result generation module 60 . DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] This application mainly solves the problems of low efficiency of existing methods, inability to reflect changes in warehouse quality, and inaccurate and incomplete evaluation results. By constructing a risk assessment model to conduct a zoning assessment of the warehouse, it provides effective quality and safety level assessment results and improves the evaluation efficiency.

[0020] In order to better understand the above technical solution, the above solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods:

[0021] Embodiment 1

[0022] like Figure 1 A warehouse quality safety level assessment method is shown, the method comprising:

[0023] Connecting to the intelligent warehouse control system, obtaining an assessment target, and obtaining a safety risk indicator of the target warehouse through the assessment target;

[0024] Specifically, first, the connection with the intelligent warehouse control system is successful through the network and communication interface, and data interaction and command control can be carried out with the control system. Obtaining the evaluation target: By interacting with the intelligent warehouse control system, specific information of the evaluation target can be obtained. The evaluation target may include the layout of the warehouse, equipment configuration, cargo type, storage requirements, etc. This information will be used for subsequent safety risk assessments. Extracting safety risk indicators: According to the evaluation target, relevant safety risk indicators are extracted from the intelligent warehouse control system. These indicators may include equipment failure rate, cargo damage rate, personnel operation error rate, safety violations, fire, natural disasters, etc. These indicators will be used to measure the safety status and potential risks of the warehouse. Analyzing safety risk indicators: Analyze the extracted safety risk indicators to identify potential safety risks and problems. Comparison and analysis can be carried out based on historical data, industry standards or similar warehouse situations to identify risks more accurately.

[0025] Collecting security information of the target warehouse, including the warehouse's floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical security events;

[0026] Specifically, collect the floor plan of the target warehouse, including the layout, size, structure and other information of the warehouse, so as to understand the overall situation and spatial distribution of the warehouse. Building layout: Understand the building layout of the warehouse, including the building structure, materials, door and window locations and other information, so as to evaluate the building quality and safety performance of the warehouse. Security equipment: Understand the security equipment of the target warehouse, including access control system, monitoring equipment, alarm system, etc., to evaluate the security capability and safety of the warehouse. Monitoring equipment: Collect the monitoring equipment information of the target warehouse, including the number, location, coverage of monitoring cameras, etc., so as to understand the monitoring capability and safety prevention effect of the warehouse. Records and reports of historical security events: Collect records and reports of historical security events of the target warehouse, including detailed information and handling results of fire, theft, accidents and other events, so as to evaluate the safety management level and historical risks of the warehouse. By collecting the above security information, we can have a comprehensive understanding of the security status of the target warehouse and provide reliable data support for the subsequent quality and safety level assessment.

[0027] Divide the target warehouse into p areas, and set multiple weights for different areas, where p is a positive integer;

[0028] Specifically, dividing the target warehouse into p areas and setting multiple weights for different areas is another effective method for evaluating the quality and safety level of the warehouse. Dividing areas: Divide the target warehouse into p areas according to the actual situation of the warehouse and the evaluation needs. These areas can be divided according to the storage characteristics of goods, the distribution of equipment, functional areas, etc. Each area should have clear boundaries and identification for subsequent evaluation and recording. Setting weights: For each area, set different weights for them according to factors such as its importance and safety risks. The setting of weights should be based on expert opinions, historical data and actual conditions to ensure their rationality and objectivity. The weight can be a numerical value or a relative proportion to reflect the relative importance of different areas in the evaluation. Assigning weight values: Assign corresponding weight values ​​to each area according to factors such as the characteristics, functions and safety risks of each area. These weight values ​​should reflect the relative contribution of the area in the overall evaluation. The weight value can be fixed or dynamically adjusted, and adjusted according to the actual changes in the warehouse and the evaluation needs. By dividing the area and setting weights, a more detailed and comprehensive quality and safety level evaluation of the target warehouse can be carried out. This approach helps to identify safety risks and hidden dangers in different areas and provide targeted improvement measures for warehouse safety management.

[0029] Building a risk assessment model through the security risk index of the target warehouse and the security information of the target warehouse;

[0030] Specifically, data collection and preprocessing: First, collect the security risk indicators and security information data of the target warehouse. These data may include historical security event records, warehouse layout, storage item types, security facility configuration, etc. Clean, organize and standardize the data to ensure data quality and consistency. Neural network model design: Select a suitable neural network model for design. Common neural network models include multi-layer perceptron, convolutional neural network, recurrent neural network, etc. According to the security risk assessment requirements of the target warehouse, determine the number of layers, number of neurons, activation function and other parameters of the neural network. Feature extraction: Use the collected security risk indicators and security information data as input features to extract features related to security risks. These features may include theft or intrusion history, fire hazards, natural disaster risks, etc. Determine the weight of each feature based on the importance of the feature. Train the neural network model: Use the training data set to train the neural network model. By adjusting the weights and biases of the neural network, the model can learn the mapping relationship between input features and output risks. Use appropriate optimization algorithms, such as gradient descent, stochastic gradient descent, etc., to train the model. Verification and testing: Apply the trained neural network model to the verification data set and test data set for verification and testing. Evaluate the model's accuracy, recall, F1 score and other indicators to evaluate the model's performance. Adjust and optimize the model based on the verification and test results. Apply the model for risk assessment: Apply the trained neural network model to the security risk assessment of the target warehouse. Input the warehouse's real-time or historical data, and calculate the risk score or level of each area through the neural network model. Based on the risk score or level, potential security risk areas and areas that need to be focused on can be identified.

[0031] Performing risk assessment on the p regions using the risk assessment model to generate q assessment results, where q is a positive integer greater than or equal to p;

[0032] Specifically, by conducting risk assessment on p areas of the target warehouse through the risk assessment model, q assessment results can be generated, where q is a positive integer greater than or equal to p. Input data: The safety risk indicators and safety information of the p areas of the target warehouse are input into the risk assessment model as input data. Regional risk assessment: The risk assessment model conducts risk assessment on each area based on the input data. The assessment results may include the safety risk level, safety hazard type, and improvement suggestions for each area. Generate assessment results: Generate q assessment results based on the output results of the risk assessment model. The q assessment results may include a detailed assessment report and a summary report for each area. Result interpretation and reporting: Explain and illustrate the generated q assessment results, such as giving the safety risk level and safety hazard type for each area. Generate corresponding reports or statements as needed to provide decision-making basis for warehouse managers. Through the above steps, the risk assessment model can be used to conduct risk assessment on p areas of the target warehouse, generate q assessment results, and provide scientific and effective quality and safety level assessment results and suggestions for warehouse managers. At the same time, the risk assessment model can also be updated and maintained according to actual conditions and needs to ensure its accuracy and effectiveness.

[0033] The overall evaluation result of the target warehouse is generated using a linear weighted model and the q evaluation results.

[0034] Specifically, the total evaluation result of the target warehouse is generated using the linear weighted model and q evaluation results, and the weight is determined: according to the importance and historical data of each evaluation indicator, a corresponding weight is assigned to each evaluation result. The weight can be a numerical value or a relative proportion, which is used to reflect the relative contribution of different evaluation results in the total evaluation. Calculate the weighted average: multiply each evaluation result by the corresponding weight, and then sum them to obtain the weighted average of the total evaluation result of the target warehouse. Determine the interpretation range of the total evaluation result: according to the calculation result of the weighted average, determine the interpretation range of the total evaluation result of the target warehouse. For example, the total evaluation result can be divided into different grades or intervals such as excellent, good, general, and poor. Provide feedback and suggestions: According to the total evaluation result, feedback and suggestions are provided to warehouse managers. For example, if the total evaluation result is general or poor, some improvement measures may need to be taken to improve the quality and safety level of the warehouse. Through the above steps, the total evaluation result of the target warehouse can be generated using the linear weighted model and q evaluation results, providing scientific and effective quality and safety level evaluation results and suggestions for warehouse managers.

[0035] Furthermore, if Figure 2 As shown, the method of the present application, the intelligent warehouse control system is connected to obtain an assessment target, and the safety risk index of the target warehouse is obtained through the assessment target. The method also includes:

[0036] Check employees' personal information, track the goods each employee is responsible for, and obtain information on abnormal theft risks;

[0037] Use big data to detect flammable items stored in warehouses and obtain abnormal fire risk information;

[0038] Based on historical natural disaster data and environmental parameter data, natural disaster prediction is performed on the target warehouse area to obtain abnormal natural disaster risk information;

[0039] The safety risk index of the target warehouse is obtained according to the abnormal theft risk information, the abnormal fire risk information and the abnormal natural disaster risk information.

[0040] Specifically, the security risk indicators of the target warehouse can be obtained, including abnormal theft risk information, abnormal fire risk information, and abnormal natural disaster risk information. These indicators can be used to evaluate the security status of the target warehouse and provide decision-making basis for warehouse managers. Abnormal theft risk information: By checking the personal information of employees and tracking the goods that each employee is responsible for, abnormal theft risk information can be obtained. For example, if the goods that an employee is responsible for are often lost or damaged, then this employee may be at risk of theft. This information can help managers to promptly discover and prevent the occurrence of theft. Abnormal fire risk information: By using big data to detect the flammability of goods stored in the warehouse, abnormal fire risk information can be obtained. For example, if the goods in certain storage areas contain dangerous goods such as flammable and explosive goods, then these areas may be at risk of fire. This information can help managers take timely measures to avoid the occurrence of fire. Abnormal natural disaster risk information: Based on historical natural disaster data and environmental parameter data, natural disasters can be predicted for the target warehouse area to obtain abnormal natural disaster risk information. For example, if the target warehouse area is in an earthquake zone or a flood-prone area, then these areas may be at risk of natural disasters. This information can help managers take timely measures to avoid losses caused by natural disasters to the warehouse. In summary, through the above steps, the safety risk indicators of the target warehouse can be obtained. These indicators can provide decision-making basis for warehouse managers, help them to timely discover and prevent the occurrence of safety risks, and ensure the safe operation of the warehouse.

[0041] Furthermore, if Figure 3 As shown, the method of the present application, the flammability detection of goods stored in the warehouse is performed through big data to obtain abnormal fire risks, and the method also includes:

[0042] Acquiring the storage goods information of the target warehouse through the intelligent warehouse control system;

[0043] Preprocessing the stored product information to obtain standard product information;

[0044] Establish a fire anomaly risk prediction model;

[0045] The flammability of the standard product information is identified by the fire abnormality risk prediction model to generate the fire abnormality risk.

[0046] Specifically, the storage goods information of the target warehouse is obtained through the intelligent warehouse control system, and the information is preprocessed to obtain the standard goods information. Then, a fire abnormality risk prediction model is established, and the flammability of the standard goods information is identified through the model to generate fire abnormality risk. The storage goods information of the target warehouse is obtained through the intelligent warehouse control system: the intelligent warehouse control system can monitor the storage goods information of the warehouse in real time, including the type, quantity, location, status, etc. of the goods. The storage goods information of the target warehouse can be obtained through the interface connection with the intelligent warehouse control system. The storage goods information is preprocessed to obtain the standard goods information: because the storage goods information may have problems such as inconsistent format and missing data, preprocessing is required. Preprocessing includes steps such as data cleaning, format conversion, and data standardization to convert the storage goods information into a standardized format for subsequent analysis and processing. Establish a fire abnormality risk prediction model: based on the standard goods information, a fire abnormality risk prediction model can be established. The model can predict the fire abnormality risk based on factors such as the flammability of the goods and the storage environment. For example, the model can determine the flammability of goods by analyzing the chemical composition, temperature, humidity and other parameters of the goods, thereby predicting the fire risk. The fire abnormality risk prediction model identifies the flammability of standard goods information and generates fire abnormality risks: input the standard goods information into the fire abnormality risk prediction model, the model can identify the flammability of the goods and generate the corresponding fire abnormality risk. This risk information can serve as an important reference for warehouse managers to conduct safety management. Through the above steps, the fire abnormality risk of the target warehouse can be identified and predicted, providing timely and accurate safety management decision support for warehouse managers.

[0047] Furthermore, the method of the present application, wherein the risk assessment model is constructed by using the security risk index of the target warehouse and the security information of the target warehouse, the method comprises:

[0048] Taking the target warehouse safety risk index and the target warehouse safety information as input features;

[0049] Extracting relevant features of security risks, wherein the relevant features include: theft, fire, and natural disasters;

[0050] The risk assessment model is obtained by training a basic model with the input features and the related features. The risk assessment model can perform risk assessment on each area according to different weights.

[0051] Specifically, the target warehouse security risk indicators and security information are used as input features to extract features related to security risks, including theft, fire, natural disasters, etc. Then, the basic model is trained through these input features and related features to obtain a risk assessment model. The risk assessment model can perform risk assessment on each area according to different weights. The target warehouse security risk indicators and security information are used as input features: the security risk indicators and security information of the target warehouse are organized into feature vectors and provided as input features for subsequent model training. The relevant features of security risks are extracted: features related to security risks, such as theft, fire, natural disasters, etc., are extracted from the input features. These features can be determined based on historical data, expert opinions, and other information. The basic model is trained through input features and related features: a basic model, such as a linear regression model, a neural network model, etc., is trained using input features and related features. Historical data can be used as a training set during the training process, and the accuracy and generalization ability of the model can be improved by optimizing the model parameters. The risk assessment model is obtained: the trained basic model can be used for risk assessment. The risk assessment results of each area are obtained by evaluating the security risk indicators and security information of each area. Conduct risk assessment for each area according to different weights: Set different weights for each area according to the importance, historical data and other information of different areas. The risk assessment model can perform weighted average or other statistical processing on each area according to these weights to obtain the final risk assessment result. Through the above steps, a risk assessment model trained based on the safety risk indicators and safety information of the target warehouse can be obtained. The model can conduct risk assessment for each area according to different weights, providing scientific and effective safety management decision support for warehouse managers.

[0052] Furthermore, the method of the present application, wherein the overall evaluation result of the target warehouse is generated by using the linear weighted model and the q evaluation results, comprises:

[0053] Normalizing the q evaluation results to obtain q standard results;

[0054] If the q standard results are greater than a preset value, the standard value is eliminated to obtain n standard results, where n is a positive integer less than q;

[0055] Calculate a weighted average value using the n standard results;

[0056] The overall evaluation result is determined based on the weighted average.

[0057] Specifically, q evaluation results are normalized to obtain q standard results. Normalization is a method of converting evaluation results into relative values, which can eliminate the influence of the differences in dimensions and numerical ranges between different evaluation indicators. Specifically, the original value of each evaluation indicator can be divided by the difference between the maximum and minimum values ​​of the indicator to obtain the normalized standard result. If the q standard results are greater than the preset value, the standard value needs to be eliminated to obtain n standard results, where n is a positive integer less than q. The preset value is a threshold set based on historical data, expert opinions and other information to determine whether the evaluation result exceeds the acceptable range. If the standard result is greater than the preset value, the evaluation result is considered unacceptable and needs to be eliminated. Then, a weighted average is calculated using the n standard results. The weighted average is calculated based on the weight of each standard result. The weight can be determined based on the importance of different evaluation indicators and historical data and other information. Each standard result can be multiplied by the corresponding weight, and then the products are added and finally divided by the sum of all weights to obtain the weighted average. Finally, the total evaluation result is determined based on the weighted average. The total assessment result is a comprehensive assessment value that reflects the overall safety status of the target warehouse. Based on the size and change trend of the weighted average value, the safety status of the target warehouse can be comprehensively assessed and analyzed to provide decision-making basis and suggestions for warehouse managers.

[0058] Furthermore, the present application method also includes:

[0059] Determine the scope of authorized personnel based on the security requirements of the target warehouse;

[0060] The facial features of authorized personnel are recorded through the intelligent access control platform, and multiple permission levels are set for personnel within the authorized personnel range;

[0061] If unauthorized personnel enter the target warehouse, call the police in time.

[0062] Specifically, according to the security requirements of the target warehouse, the scope of authorized personnel can be determined. Through the intelligent access control platform, the facial features of authorized personnel can be recorded, and multiple permission levels can be set for personnel within the scope of authorized personnel. If an unauthorized person enters the target warehouse, the system can issue an alarm in time. Determine the scope of authorized personnel according to the security requirements of the target warehouse: The security requirements of the target warehouse can be set according to the actual situation, such as classifying the goods in the warehouse to determine which goods require a higher level of security protection. According to these requirements, it can be determined which personnel need authorization to enter the warehouse. Record the facial features of authorized personnel through the intelligent access control platform: The intelligent access control platform can integrate cameras and face recognition technology to record the facial features of authorized personnel. When a person within the scope of authorized personnel enters the warehouse, the system automatically recognizes his facial features and verifies his identity. Set multiple permission levels for people within the scope of authorized personnel: According to the security requirements of the target warehouse and the identity of the authorized personnel, different permission levels can be set for authorized personnel. For example, a high-level authorized person can access more warehouse areas, while a low-level authorized person can only access specific areas. If an unauthorized person enters the target warehouse, an alarm is issued in time: If a person attempts to enter the target warehouse but is not authorized, the intelligent access control platform will automatically issue an alarm. This can include sound alarms, flashing lights or other forms of warnings to attract the attention of warehouse managers or security personnel. Through the above measures, access rights to the target warehouse can be effectively controlled to ensure that only authorized personnel can enter the warehouse, and different permission levels can be set as needed. This will help improve the security level of the target warehouse.

[0063] Embodiment 2

[0064] Based on the same inventive concept as the warehouse quality safety level assessment method in the above embodiment, Figure 4 As shown, the present application provides a warehouse quality safety level assessment system, the system comprising:

[0065] A safety risk indicator acquisition module 10, which is used to connect to the intelligent warehouse control system, obtain an assessment target, and obtain a safety risk indicator of a target warehouse through the assessment target;

[0066] A security information collection module 20, which is used to collect security information of the target warehouse, including the warehouse's floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical security events;

[0067] A region division module 30, the region division module 30 is used to divide the target warehouse into p regions, and set multiple weights for different regions, wherein p is a positive integer;

[0068] A risk assessment model building module 40, wherein the risk assessment model building module 40 is used to build a risk assessment model through the security risk index of the target warehouse and the security information of the target warehouse;

[0069] A risk assessment module 50, wherein the risk assessment module 50 is used to perform risk assessment on the p areas through the risk assessment model to generate q assessment results, where q is a positive integer greater than or equal to p;

[0070] The total evaluation result generating module 60 is used to generate the total evaluation result of the target warehouse by using the linear weighted model and the q evaluation results.

[0071] Furthermore, the system also includes:

[0072] The module for obtaining the security risk index is used to retrieve the personal information of employees, track the goods that each employee is responsible for, and obtain the abnormal risk information of theft; perform flammability detection on the goods stored in the warehouse through big data to obtain the abnormal risk information of fire; predict natural disasters for the target warehouse area based on historical natural disaster data and environmental parameter data to obtain the abnormal risk information of natural disasters; obtain the security risk index of the target warehouse based on the abnormal risk information of theft, abnormal risk information of fire, and abnormal risk information of natural disasters.

[0073] Furthermore, the system also includes:

[0074] The flammability judgment module is used to obtain the storage product information of the target warehouse through the intelligent warehouse control system; pre-process the storage product information to obtain standard product information; establish a fire abnormality risk prediction model; identify the flammability of the standard product information through the fire abnormality risk prediction model to generate the fire abnormality risk.

[0075] Furthermore, the system also includes:

[0076] The regional risk assessment result acquisition module is used to take the target warehouse safety risk index and the target warehouse safety information as input features; extract relevant features of safety risks, the relevant features include: theft, fire, natural disasters; train a basic model through the input features and the relevant features to obtain the risk assessment model, and the risk assessment model can perform risk assessment on each area according to different weights.

[0077] Furthermore, the system also includes:

[0078] The module for determining the overall evaluation result is used to normalize the q evaluation results to obtain q standard results; if the q standard results are greater than a preset value, the standard value is eliminated to obtain n standard results, wherein n is a positive integer less than q; a weighted average is calculated using the n standard results; and the overall evaluation result is determined based on the weighted average.

[0079] Furthermore, the system also includes:

[0080] The permission setting module is used to determine the scope of authorized personnel according to the security requirements of the target warehouse; record the facial features of authorized personnel through the intelligent access control platform, and set multiple permission levels for personnel within the authorized personnel range; if an unauthorized person enters the target warehouse, an alarm is issued in time.

[0081] Through the detailed description of the aforementioned warehouse quality and safety level assessment method, the technical personnel in this field can clearly understand a warehouse quality and safety level assessment system in this embodiment. For the system disclosed in the embodiment, since it corresponds to the device disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0082] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A warehouse quality safety level assessment method, characterized in that: The method comprises: Connecting to the intelligent warehouse control system, obtaining an assessment target, and obtaining a safety risk indicator of the target warehouse through the assessment target; Collecting security information of the target warehouse, including the warehouse's floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical security events; Divide the target warehouse into p areas, and set multiple weights for different areas, where p is a positive integer; Building a risk assessment model through the security risk index of the target warehouse and the security information of the target warehouse; Performing risk assessment on the p regions using the risk assessment model to generate q assessment results, where q is a positive integer greater than or equal to p; The overall evaluation result of the target warehouse is generated using a linear weighted model and the q evaluation results.

2. The method according to claim 1, characterized in that The method further comprises: connecting to the intelligent warehouse control system, obtaining an assessment target, and obtaining a safety risk index of the target warehouse through the assessment target. Check employees' personal information, track the goods each employee is responsible for, and obtain information on abnormal theft risks; Use big data to detect flammable items stored in warehouses and obtain abnormal fire risk information; Based on historical natural disaster data and environmental parameter data, natural disaster prediction is performed on the target warehouse area to obtain abnormal natural disaster risk information; The safety risk index of the target warehouse is obtained according to the abnormal theft risk information, the abnormal fire risk information and the abnormal natural disaster risk information.

3. The method according to claim 2, characterized in that The method of performing flammability detection on goods stored in the warehouse by using big data to obtain abnormal fire risk also includes: Acquiring the storage goods information of the target warehouse through the intelligent warehouse control system; Preprocessing the stored product information to obtain standard product information; Establish a fire anomaly risk prediction model; The flammability of the standard product information is identified by the fire abnormality risk prediction model to generate the fire abnormality risk.

4. The method according to claim 1, characterized in that The risk assessment model is constructed by using the safety risk index of the target warehouse and the safety information of the target warehouse, and the method includes: Taking the target warehouse safety risk index and the target warehouse safety information as input features; Extracting relevant features of security risks, wherein the relevant features include: theft, fire, and natural disasters; The risk assessment model is obtained by training a basic model with the input features and the related features. The risk assessment model can perform risk assessment on each area according to different weights.

5. The method according to claim 1, characterized in that The method of generating a total evaluation result of the target warehouse by using the linear weighted model and the q evaluation results comprises: Normalizing the q evaluation results to obtain q standard results; If the q standard results are greater than a preset value, the standard value is eliminated to obtain n standard results, where n is a positive integer less than q; Calculate a weighted average value using the n standard results; The overall evaluation result is determined based on the weighted average.

6. The method according to claim 1, characterized in that Also includes: Determine the scope of authorized personnel based on the security requirements of the target warehouse; The facial features of authorized personnel are recorded through the intelligent access control platform, and multiple permission levels are set for personnel within the authorized personnel range; If unauthorized personnel enter the target warehouse, call the police in time.

7. A warehouse quality safety level assessment system, characterized in that: The system comprises: A safety risk indicator acquisition module, which is used to connect to the intelligent warehouse control system, obtain an assessment target, and obtain a safety risk indicator of a target warehouse through the assessment target; A security information collection module, which is used to collect security information of the target warehouse, including the warehouse's floor plan, building layout, security equipment, monitoring equipment, and records and reports of historical security events; A region division module, the region division module is used to divide the target warehouse into p regions, and set multiple weights for different regions, wherein p is a positive integer; A risk assessment model building module, wherein the risk assessment model building module is used to build a risk assessment model through the safety risk index of the target warehouse and the safety information of the target warehouse; A risk assessment module, the risk assessment module is used to perform risk assessment on the p areas through the risk assessment model to generate q assessment results, where q is a positive integer greater than or equal to p; A total evaluation result generation module is used to generate a total evaluation result of the target warehouse using a linear weighted model and the q evaluation results.

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