Data security protection verification method and system for environment field monitoring equipment

Abstract

The invention relates to a data security protection verification method and system for environment field monitoring equipment, and belongs to the technical field of environment field monitoring. The method comprises the following steps: acquiring evaluation data of a positioning and time service accuracy index; performing penetration test on the interface and the storage medium of the environment field monitoring equipment to obtain evaluation data of sealing safety indexes of the interface and the storage medium; acquiring evaluation data of the database encryption protection indexes by adopting a security scanning tool and an encryption analysis tool; simulating the operation scene, and obtaining evaluation data of the running operation log record index; and automatically judging the compliance of the data based on the convolutional neural network model, and generating a data security protection level quantitative evaluation result of the environment field monitoring equipment based on the evaluation data of each index in the multi-dimensional quantitative evaluation index set. According to the environment field monitoring equipment data security verification method and system, the monitoring equipment data security protection level can be quantified, and monitoring data security improvement is promoted.

Description

Technical Field

[0001] The present application belongs to the technical field of environmental field monitoring, and specifically relates to a data security protection verification method and system for environmental field monitoring equipment. Background Art

[0002] With the increasing awareness of environmental protection and increasingly stringent regulations, on-site monitoring equipment for pollution sources plays a vital role in environmental monitoring and governance.

[0003] Existing monitoring equipment has many shortcomings in data security and anti-counterfeiting functions, and there is also a lack of comprehensive assessment methods for the evaluation and verification of data security prevention and control of monitoring equipment: data security assessments of environmental monitoring equipment often focus on a single dimension, such as only focusing on data encryption or interface security; feedback on the safety status of the equipment and real-time monitoring are not timely or effective; the integrity and reliability of monitoring data during transmission and storage are major risk links, but related verification is blank, the data security prevention and control of monitoring equipment cannot be quantitatively evaluated, and the security of monitoring data cannot be guaranteed, affecting the scientificity and effectiveness of environmental management.

[0004] Therefore, there is an urgent need for a data security protection verification method for pollution source on-site monitoring equipment to comprehensively, objectively and effectively evaluate and verify its data security function, guide on-site monitoring to selectively select equipment that meets the needs, and ensure the authenticity and credibility of environmental monitoring data. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a data security protection verification method and system for environmental field monitoring equipment, which comprehensively characterizes the data security performance of the monitoring equipment to solve the problems of accuracy and security of information in existing pollution source field monitoring equipment.

[0006] In a first aspect of the present application, a data security protection verification method for an environmental field monitoring device is provided, comprising: Integrate high-precision positioning and time synchronization equipment on the environmental field monitoring equipment in advance, use the high-precision positioning and time synchronization equipment to automatically collect time and positioning information of the environmental field monitoring equipment, calculate positioning error and time error respectively, and obtain evaluation data of positioning and timing accuracy indicators; Conduct penetration tests on the interfaces and storage media of environmental field monitoring equipment to verify whether the interfaces and storage media can identify destructive behaviors and obtain evaluation data on the sealing security indicators of the interfaces and storage media; Use security scanning tools and encryption analysis tools to verify the encryption strength and access control mechanism of the database in the environmental field monitoring equipment, and obtain evaluation data of the database encryption protection index; Simulate operation scenarios, evaluate whether environmental on-site monitoring equipment fully records operation logs, and obtain evaluation data on operation log recording indicators; Based on the evaluation data of each indicator in the multidimensional quantitative evaluation indicator set, a data security quantitative evaluation result of the environmental field monitoring equipment is generated. The multidimensional quantitative evaluation indicator set includes at least: positioning and timing accuracy indicators, interface and storage medium sealing security indicators, database encryption protection indicators and operation log recording indicators.

[0007] Optionally, it also includes: Obtain monitoring logs and monitoring process data of environmental field monitoring equipment in real time during operation; Input the monitoring log and monitoring process data into a pre-established environmental field monitoring equipment data compliance evaluation model to obtain a data-assisted compliance evaluation result, and automatically generate a data security protection verification report in combination with the data security quantitative evaluation result of the environmental field monitoring equipment for real-time feedback; Among them, the environmental field monitoring equipment data compliance evaluation model is a neural network model that adopts an association rule mining model. The environmental field monitoring equipment data compliance evaluation model is pre-trained using monitoring training data with different emission characteristics, emission factors and monitoring methods with labels to predict whether the input data complies with the environmental monitoring compliance standards.

[0008] Optionally, the neural network model adopts a multi-layer deep convolutional neural network architecture, including an input layer, two hidden layers, and an output layer; Among them, the preprocessed raw data is extracted through features, and the vectorized features become the input layer; the hidden layer adopts the CNN convolutional neural network mode, which is divided into two layers: the convolution layer and the pooling layer; the convolution layer is responsible for performing convolution operations on the data of the input layer, and convolution calculations are performed with the input data through sliding convolution kernels, and feature information in the input data is extracted during the convolution process; the filter of the convolution layer selects a filter of size 128; the pooling layer downsamples the output of the convolution layer, and the pooling method is bit average pooling, the average value of all values ​​in the area; the output layer performs forward calculations, and the calculation results are obtained by passing the raw data through weight calculations and activation function constraints to the output layer; the Relu function is selected as the activation function.

[0009] Optionally, the multi-dimensional quantitative evaluation index set also includes: key parameter modification and calibration deviation limit index, data acquisition and transmission completeness, timeliness and accuracy index, and original record printing anti-counterfeiting index; Combine the obtained device permission management score, calibration deviation control score, and read-only record and anti-tampering score to calculate and obtain the evaluation data of key parameter modification and calibration deviation limit indicators; Combine the acquired data integrity score, data real-time score, and data security score to calculate and obtain evaluation data on the completeness, timeliness, and accuracy of data collection and transmission; The obtained encryption algorithm and data security score, anti-counterfeiting code authenticity and integrity score, and physical anti-counterfeiting measures score are combined for calculation to obtain evaluation data of the original record printing anti-counterfeiting index.

[0010] Optionally, based on the evaluation data of each indicator in the multi-dimensional quantitative evaluation indicator set, generating the data security quantitative evaluation result of the environmental field monitoring equipment includes: Perform weighted calculation on each indicator in the multi-dimensional quantitative evaluation indicator set to generate the data security quantitative evaluation results of the environmental field monitoring equipment; The weight of each indicator is determined by the following method: The results are obtained comprehensively based on the different impacts of different assessment data on on-site detection of pollution sources.

[0011] Optionally, the obtaining of evaluation data of positioning and timing accuracy indicators includes: Use high-precision time synchronization equipment to test the time error between the time displayed by the on-site monitoring equipment in the environment and the standard time, and obtain a time accuracy score based on the time error; Using a high-precision positioning synchronization device to measure the actual position of the environmental field monitoring device, calculating the position error between the actual position and the reported position, and obtaining a position accuracy score based on the position error; Use a simulator to simulate different degrees of satellite signal obstruction or interference, record whether an alarm is generated and whether the event is accurately recorded in the case of satellite signal obstruction or interference, and obtain an anti-interference ability score based on the recording results; Use penetration testing tools to monitor network traffic and obtain security scores based on whether modified interfaces or instructions are found; The time accuracy score, position accuracy score, anti-interference ability score and safety score are weightedly calculated to obtain evaluation data A1 of the positioning and timing accuracy index.

[0012] Optionally, the acquisition of evaluation data of the interface and storage medium sealing safety index includes: Identify all communication interfaces of environmental field monitoring equipment, including wired and wireless interfaces; Use data monitoring tools to connect between field monitoring equipment and data acquisition terminals or platforms to monitor the data transmission process; Through non-periodic data acquisition methods, query instructions are actively sent to on-site monitoring equipment to obtain real-time monitoring data; Use analysis tools to parse the monitoring data frames and automatically identify whether the data encryption method is plain text, frame data encryption, or byte-by-byte encryption; Automatically identify the monitored data, compare the data with the system's built-in database, and identify the integrity of the data; Use monitoring tools to simulate data delivery and perform a second read to verify whether the data can be tampered with; Based on whether the data can be monitored, the encryption method of the data, the integrity of the data, and whether the data has been tampered with, the evaluation data A2 of the sealing security index of the interface and the storage medium is obtained.

[0013] Optionally, obtaining evaluation data of database encryption protection indicators includes: By simulating authorized and unauthorized access attempts, the effectiveness of access control is verified and access control scores are obtained; Use encryption algorithm assessment tools to test the strength of the encryption algorithm used in the database and obtain the encryption algorithm strength score; Evaluate the security of each process in the key lifecycle management and obtain the key management mechanism score; Use data recovery tools to recover data from encrypted storage, check whether the data can be illegally recovered, and obtain the encrypted storage integrity score; The access control score, encryption algorithm strength score, key management mechanism score and encryption storage integrity score are weightedly calculated to obtain evaluation data A3 of the database encryption protection index.

[0014] Optionally, obtaining evaluation data of device operation log record indicators includes: Use log analysis tools to analyze the collected logs to check whether there are any missing key event records and obtain the log integrity score; Use the timestamp verification tool to verify the timestamp in the log, evaluate the consistency between the timestamp recorded in the log and the actual operation time, and obtain the timestamp accuracy score; Use permission management tools to test the access control settings of log files, evaluate the access permission management of log files, and obtain access permission control scores; The log integrity score, timestamp accuracy score and access permission control score are weightedly calculated to obtain evaluation data A4 of the operation log record index.

[0015] According to a second aspect of the present application, a data security protection verification system for environmental field monitoring equipment is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any one of the above-mentioned data security protection verification methods for environmental field monitoring equipment is implemented.

[0016] The data security protection verification method for environmental field monitoring equipment provided in this application evaluates and verifies monitoring activities through high-precision positioning and time synchronization technology, ensuring the authenticity of monitoring activities and the synchronization of monitoring data. Through multi-level security measures, physical and network-level security measures are implemented, including interface closure, data encryption, permission control, etc., to comprehensively protect equipment and data security. By establishing a multi-dimensional quantitative evaluation index system, the data security prevention and control functions of field monitoring equipment are comprehensively and objectively evaluated to monitor the security status of the equipment in real time and respond quickly when problems are found. It not only solves the technical defects of existing environmental monitoring equipment in data security and real-time monitoring, but also improves the overall monitoring efficiency and reliability. In addition, the present application also provides a data security protection verification system for environmental field monitoring equipment with the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of this specification or the technical solutions in 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 described below are only some embodiments recorded in the embodiments of this specification. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 A flowchart of a specific implementation method of the data security protection verification method for environmental field monitoring equipment provided by this application; Figure 2 A flowchart for obtaining evaluation data of positioning and timing accuracy indicators for an embodiment of the present application; Figure 3 Flow chart for obtaining evaluation data of interface and storage medium sealing security indicators for this application; Figure 4 Flowchart for obtaining evaluation data of database encryption protection indicators for this application; Figure 5 A flowchart for obtaining evaluation data for the operation log record indicators for this application; Figure 6 A flowchart for obtaining compliance assessment of environmental field testing data for this application; Figure 7 This is a structural block diagram of the data security protection verification system for the environmental field monitoring equipment provided in this application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. It should be noted that, in the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be combined, separated, interchanged and / or rearranged with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] The terms used here are for the purpose of describing specific embodiments, and are not intended to be restrictive. As used here, unless the context clearly indicates otherwise, the singular forms "one (kind, person)" and "said (the)" are also intended to include plural forms. In addition, when the terms "comprise" and / or "include" and their variations are used in this specification, it is explained that there are stated features, integral bodies, steps, operations, parts, assemblies and / or their groups, but it is not excluded that there are or add one or more other features, integral bodies, steps, operations, parts, assemblies and / or their groups. It should also be noted that, as used here, the terms "substantially", "approximately" and other similar terms are used as approximate terms and not as degree terms, so that they are used to explain the inherent deviations of the measured values, calculated values ​​and / or the values ​​provided that will be recognized by those of ordinary skill in the art.

[0021] A flowchart of a specific implementation method of the data security protection verification method for environmental field monitoring equipment provided in this application is as follows Figure 1 As shown, the method specifically includes: S101: pre-integrate a high-precision positioning and time synchronization device on the environmental field monitoring device, use the high-precision positioning and time synchronization device to automatically collect the time and positioning information of the environmental field monitoring device, perform positioning error calculation and time error calculation respectively, and obtain evaluation data of positioning and timing accuracy indicators.

[0022] It can be understood that the environmental field monitoring equipment in this application is equipment installed at the pollution source site.

[0023] Select applicable high-precision positioning and time synchronization equipment, such as the high-precision Beidou satellite positioning system and the Network Time Protocol (NTP), and integrate these devices on the environmental field inspection tools in advance. Use these tools to accurately measure the position and time of the monitoring equipment, automatically collect the time and positioning information of the environmental field monitoring equipment, calculate the positioning error and time error respectively, and obtain evaluation data on the positioning and timing accuracy indicators.

[0024] S102: Perform penetration testing on the interface and storage medium of the environmental field monitoring equipment to verify whether the interface and storage medium can identify destructive behavior and obtain evaluation data of the sealing security index of the interface and storage medium.

[0025] Identify all interfaces (such as USB, Ethernet, etc.) and storage media (such as built-in hard disk, SD card, etc.) of the monitoring device, use penetration testing tools and techniques to attempt unauthorized access and data tampering on these interfaces and storage media, verify their security, and record the response and defense capabilities of the device under test to evaluate the sealing security of the interfaces and storage media.

[0026] S103: Use security scanning tools and encryption analysis tools to verify the encryption strength and access control mechanism of the database in the environmental field monitoring equipment, and obtain evaluation data of the database encryption protection index.

[0027] Use security scanning tools to scan the database in the device to check for existing security vulnerabilities and risks. Evaluate the security of the encryption algorithm and key management used in the database, and verify the strength of encrypted data and access control mechanisms.

[0028] S104: Simulate an operation scenario to operate the equipment, evaluate whether the environmental field monitoring equipment records the operation log comprehensively, and obtain evaluation data of the operation log recording indicators.

[0029] Simulate various normal and abnormal operation scenarios, such as data input, configuration changes, and system restarts. Check whether the system can fully and accurately record the details of all operations, including operation time, operator, operation content, and changes in information content before and after the operation. Automatically obtain log information and evaluate the secure storage and tamper-proof settings of device logs.

[0030] S105: Generate a data security quantitative evaluation result of the environmental field monitoring equipment based on the evaluation data of each indicator in the multidimensional quantitative evaluation indicator set, wherein the multidimensional quantitative evaluation indicator set at least includes: positioning and timing accuracy indicator, interface and storage medium sealing security indicator, database encryption protection indicator and operation log recording indicator.

[0031] Collect data from each test, and each test provides quantitative indicators about its security dimension. According to the evaluation data of each indicator in the multi-dimensional quantitative evaluation indicator set, the data security quantitative evaluation results are finally obtained. Furthermore, the current security level can be determined based on the correspondence between the data security quantitative evaluation results and the security level. According to the change of security level, the corresponding security response measures are automatically triggered, such as issuing alarms, automatic on-site sealing, etc.

[0032] As a specific implementation method, the data security verification method of pollution source field monitoring equipment can further include: based on a pre-established environmental field monitoring equipment data compliance evaluation model, obtaining a data-assisted compliance evaluation result, automatically generating a data security protection verification report in combination with the data security quantitative evaluation result of the environmental field monitoring equipment, and providing real-time feedback. Among them, the environmental field monitoring equipment data compliance evaluation model uses a convolutional neural network model to evaluate the compliance of the data.

[0033] The specific implementation process of this process is: real-time acquisition of monitoring logs and monitoring process data during the operation of environmental field monitoring equipment; inputting the monitoring logs and monitoring process data into a pre-established environmental field monitoring equipment data compliance evaluation model to obtain data-assisted compliance evaluation results, and automatically generating a data security protection verification report in combination with the data security quantitative assessment results of the environmental field monitoring equipment for real-time feedback.

[0034] Among them, the environmental field monitoring equipment data compliance evaluation model is a neural network model that adopts an association rule mining model. The environmental field monitoring equipment data compliance evaluation model is pre-trained using monitoring training data with different emission characteristics, emission factors and monitoring methods with labels to predict whether the input data complies with the environmental monitoring compliance standards.

[0035] In the embodiment of the present application, the neural network model adopts an association rule mining model to continuously obtain monitoring logs and monitoring process data under different emission characteristics, emission factors and monitoring methods, generate monitoring logs for rationality judgment, and the result prediction of the monitoring process data is matched with the on-site monitoring data to obtain the data compliance evaluation result.

[0036] The specific implementation process can be divided into: (1) Data preprocessing: The monitoring results of the on-site monitoring equipment, system logs, system calibration variables and pollution characteristic factors emitted by different industries are selected as the database. During the operation, the latest data continuously obtained in the system is used as training data. After the training data is determined, the data needs to be cleaned to remove outliers and missing values. Then, the data is standardized or normalized to ensure that the numerical ranges between features are consistent to avoid excessive influence of certain features on the model. The preprocessed model data is used as a unified training data set.

[0037] (2) Construct the architecture of the model. The neural network model adopts a multi-layer deep convolutional neural network architecture, which includes an input layer, two hidden layers, and an output layer.

[0038] Input layer: The preprocessed raw data is extracted through features, and the vectorized features become the input layer.

[0039] Hidden layer: It adopts CNN convolutional neural network mode, which is divided into two layers: convolution layer and pooling layer.

[0040] Convolution layer: responsible for convolution operation on the data of the input layer, convolution operation is performed with the input data through sliding convolution kernel, and feature information in the input data is extracted during the convolution process. The filter of the convolution layer is selected as 128-size filter, because there are many variables that need to be convolutionally calculated in this system, and the filter of 128 can both carry out deep calculation and effectively avoid overfitting.

[0041] Pooling layer: downsamples the output of the convolutional layer to reduce the number of parameters, reduce the size of the model, and avoid overfitting. In this application, the pooling method is bit average pooling, the average value of all values ​​in the region.

[0042] Output layer: forward calculation is performed, and the original data is subjected to weight calculation and activation function constraint to obtain the calculation result at the output layer. Because there are many types of data in this application, the Relu function with better sparsity characteristics is selected as the activation function, which can further improve the activation rate.

[0043] The difference between the actual result and the model calculation result is calculated according to the loss function, and the difference is given to the optimization function; the optimization function corrects the weight parameters layer by layer through back propagation to complete a round of model training operation; iterates 3-6 steps repeatedly until the loss value drops to an acceptable range to complete the model training and obtain the final model.

[0044] In order to further improve the accuracy of data classification calculation process, this application selects the method of using softmax loss function and cross entropy loss function together, and uses the cross entropy loss function to effectively measure the difference between the model output probability distribution and the true label.

[0045] It can be understood that the neural network model in this application is deployed on the system server, which aggregates the monitoring data of each terminal node in the environmental monitoring device and performs continuous iterative optimization.

[0046] The data security verification method provides a data security framework and basic protection, while the neural network model performs advanced data processing and analysis on this basis to enhance the intelligent processing capabilities of data. Through intelligent means, the monitoring equipment's ability to predict data anomalies and automatically judge compliance is enhanced. This combination provides a comprehensive, efficient and intelligent solution to meet complex and changing environmental monitoring needs.

[0047] In addition, the multidimensional quantitative evaluation indicator set can further include: key parameter modification and calibration deviation limit indicators, data collection and transmission completeness, timeliness and accuracy indicators, and original record printing anti-counterfeiting indicators.

[0048] Among them, the obtained device permission control management score, calibration deviation control score, read-only record and anti-tampering score are combined for calculation to obtain the evaluation data of key parameter modification and calibration deviation limit indicators.

[0049] The obtained data integrity score, data real-time score and data security score are combined for calculation to obtain evaluation data of the completeness, timeliness and accuracy indicators of data collection and transmission.

[0050] The obtained encryption algorithm and data security score, anti-counterfeiting code authenticity and integrity score, and physical anti-counterfeiting measures score are combined for calculation to obtain evaluation data of the original record printing anti-counterfeiting index.

[0051] As another specific implementation method, the process of generating a data security quantitative evaluation result of an environmental field monitoring device based on the evaluation data of each indicator in a multidimensional quantitative evaluation indicator set can be obtained in the following manner: performing a weighted calculation on each indicator in the multidimensional quantitative evaluation indicator set to generate a data security quantitative evaluation result of an environmental field monitoring device.

[0052] It is understandable that different weights can be assigned in advance according to the importance of each indicator. For example, according to the different impacts of different assessment data on on-site detection of pollution sources, the weights can be obtained as follows: The weight of positioning and timing accuracy indicator A1 is: 10% The weight of interface and storage medium sealing safety index A2 is: 15% The weight of the database encryption protection indicator A3 is: 15% The weight of the operation log record indicator A4 is: 15% The weight of the key parameter modification and calibration deviation limit indicator A5 is: 15% The weight of the indicator A6 for complete, timely and accurate data collection and transmission is: 10% The weight of data-assisted compliance assessment result A7 is: 10% The weight of the original record printing anti-counterfeiting index A8 is: 10% (A1, A2, A3, A4, A5, A6, A7, A8 all have a full score of 100) The final data security quantitative assessment result = A1 * 0.1 + A2 * 0.15 + A3 * 0.15 + A4 *0.15 + A5 * 0.15 + A6 * 0.1 + A7 * 0.1+A8*0.1.

[0053] In order to comprehensively and systematically evaluate the data security (anti-counterfeiting) function of on-site monitoring equipment for pollution sources, this application constructs a multi-dimensional quantitative indicator system and provides specific data security protection verification methods, in which each indicator can be accurately and objectively evaluated.

[0054] Specifically, the high-precision Beidou satellite positioning system and the Network Time Protocol (NTP) server can be combined to locate and calibrate the environmental field monitoring equipment, restrict the positioning information and time modification functions, set an alarm message when the positioning and timing function module fails or is shut down, and perform read-only recording.

[0055] like Figure 2 As shown in the flowchart of the embodiment of the present application for obtaining the evaluation data of the positioning and timing accuracy index, the present application provides a specific process for obtaining the evaluation data of the positioning and timing accuracy index: S201: Use a high-precision time synchronization device (such as an NTP client) to test the time error between the time displayed by the on-site monitoring device in the environment and the standard time, and obtain a time accuracy score based on the time error.

[0056] The monitoring equipment automatically obtains timing information after it is turned on. This can be a timestamp from the Beidou system or the accurate time obtained through the network timing service. The timestamp is a key factor in the integrity and legality verification of monitoring data.

[0057] Calculate the difference between the time of the built-in clock of the monitoring device and the obtained time stamp to detect the accuracy and reliability of the instrument clock. The scoring mechanism can be specifically: if the clock error is within 5 seconds, the score is 45 points (full score). If the error exceeds 5 seconds, 1 point will be deducted for every 2 seconds.

[0058] S202: Use a high-precision positioning synchronization device (such as a GNSS receiver) to measure the actual position of the environmental field monitoring device, calculate the position error between the actual position and the position reported by the monitoring device, and obtain a position accuracy score based on the position error.

[0059] After the instrument is turned on, it automatically obtains the latitude and longitude information of the current location through the Beidou satellite system or through an Internet connection. This automatic function ensures that the instrument can quickly and accurately locate its position every time it is turned on. The Euclidean distance between the latitude and longitude automatically obtained by the instrument and the latitude and longitude of the actual high-precision positioning device is calculated for evaluation.

[0060] The calculation formula is: Error = Among them, x1 and y1 are the longitude and latitude of high-precision positioning information, and x2 and y2 are the longitude and latitude information of the equipment positioning. The scoring standard is: if the positioning error is less than 1 meter, this item is scored 45 points; if it is greater than 1 meter, one point will be deducted for every 0.5 meter increase in error.

[0061] S203: Use a simulator to simulate different degrees of satellite signal obstruction or interference, record whether the monitoring device generates an alarm and whether the event is accurately recorded when the satellite signal is blocked or interfered, and obtain an anti-interference ability score based on the device response and recording results.

[0062] A specific scoring method can be: (1) If the device can accurately detect all interference and generate an alarm, the anti-interference ability score is a full score of 10 points; (2) If the device can detect some interference but fails to generate an alarm, the anti-interference ability score is 5 points; (3) If the device fails to detect interference or fails to record it correctly, the anti-interference ability score is 0 points.

[0063] S204: Use a penetration testing tool (such as Wireshark) to monitor network traffic and obtain a security score based on whether a modification interface or instruction is found. If there is a modifiable security vulnerability, no score will be given.

[0064] A specific scoring method can be: (1) If no suspicious modification interfaces or instructions are found, the security score is a full 10 points; (2) If interfaces or instructions with potential risks are found, the security score is 5 points; (3) If interfaces that can directly modify location or time are found, the security score is 0 points.

[0065] S205: Perform weighted calculation on the time accuracy score, position accuracy score, anti-interference ability score and safety score to obtain evaluation data A1 of the positioning and timing accuracy index.

[0066] The determination method of the evaluation data A1 of the above positioning and timing accuracy indicators takes into account multiple aspects of positioning and timing accuracy, providing a more comprehensive evaluation standard. By combining time and position accuracy, anti-interference ability and security, it can more accurately reflect the performance of the device in different environments.

[0067] Specifically, the assessment content of the sealing security indicators of interfaces and storage media is as follows: implementing strict sealing measures for all physical interfaces (such as USB, serial ports, network interfaces, wireless interfaces) and storage media (such as hard disks, SD cards), using one-way transmission physical isolation, disposable sealing labels, electronic locks, welding fixed to circuit boards, etc.

[0068] like Figure 3 As shown in the flowchart of the present application for obtaining the evaluation data of the interface and the storage medium sealing safety index, the present application provides a specific process for obtaining the evaluation data of the interface and the storage medium sealing safety index: S301: Identify all communication interfaces of the on-site detection equipment, including wired interfaces and wireless interfaces.

[0069] The interfaces include wired data interfaces such as RS485, RS232, RJ45, etc. for external communication of the on-site detection equipment and wireless data interfaces such as WIFI, Bluetooth, 433M wireless, etc.

[0070] S302: Use a data monitoring tool to connect between the on-site detection equipment and the data acquisition terminal or platform to monitor the data transmission process.

[0071] Through a dedicated test tool, data monitoring is connected between the detection device (referred to as the slave device) and the data acquisition terminal or platform (referred to as the master device).

[0072] S303: Actively send a query command to the on-site detection equipment through a non-periodic data acquisition method to obtain real-time monitoring data.

[0073] In the acquisition of non-periodic data, the non-periodic data can be obtained by actively sending query instructions. The security of the encryption method can be evaluated by frame parsing of the monitored data.

[0074] S304: Perform frame parsing on the monitoring data through a parsing tool to automatically identify whether the data encryption method is plain text, frame data encryption, or byte-by-byte encryption.

[0075] The monitored data is automatically analyzed using a variety of encoding methods. If FLOAT characters, ASCII codes, etc. can be directly analyzed, it is determined to be plain text data. CRC and LRC frame data check encryption are determined to be frame data encryption. Byte-by-byte encryption is determined to be single-byte encryption.

[0076] S305: Automatically identify the monitored data, traverse and compare the data with the database embedded in the system, and identify the integrity of the data.

[0077] The monitored data is automatically identified by traversing, comparing, identifying and analyzing the database embedded in the system to identify whether the transmitted data contains: process detection data, quality control data, operation log data, basic information of instruments and equipment (serial number, software and hardware versions), etc., to determine the integrity of the data.

[0078] S306: Use a monitoring tool to simulate data delivery and perform a second read to verify whether the data can be tampered with.

[0079] A monitoring tool is used to simulate data delivery, and a second reading is performed after delivery to verify whether data tampering can be performed by transmitting data.

[0080] S307: Based on whether the data can be monitored, the encryption method of the data, the integrity of the data, and whether the data has been tampered with, the evaluation data A2 of the sealing security index of the interface and the storage medium is obtained.

[0081] A specific scoring rule is: (the total score of item A2 is 100 points) The data can be monitored, the information is sent in plain text, and the score of item A2 is 10 points; The data can be monitored, and the information is sent as ciphertext. The encryption method is performed on each frame of data (that is, the entire frame of data is encrypted once). The score of A2 data is 20 points; The data can be monitored, using dynamic keys and other methods to encrypt each byte. The A2 data score is 30 points; For data encryption only for test results, the score for item A2 is 10 points; The data transmission contains specific information. For each additional piece of information, the score of item A2 will increase by 10 points. Specific information includes: process detection data, quality control data, operation log data, basic information of instruments and equipment (serial number, software and hardware version), etc. If the data transmission address can be identified and verified, the score of item A2 will increase by 10 points. If the data can be tampered through data transmission, item A2 will not be scored. If the data cannot be tampered with, the score of item A2 will be 10 points.

[0082] Each score is accumulated according to the actual situation of the device, up to 100 points. The above scoring rules are used to comprehensively evaluate the device's protection capabilities in data transmission and interface security. The higher the score, the stronger the data transmission security of the device, which can effectively prevent data from being monitored, cracked or tampered with.

[0083] Specifically, the evaluation content of the database encryption protection index is: evaluate the database access control, encryption algorithm strength, key management mechanism and encryption storage integrity of the monitoring equipment database to ensure that the data is not illegally read or tampered with during the storage process. Analyze the encryption algorithm (such as AES, RSA), key management mechanism and encryption storage integrity of the monitoring equipment database.

[0084] like Figure 4 As shown in the flowchart of obtaining the evaluation data of the database encryption protection index in this application, this application provides a specific process for obtaining the evaluation data of the database encryption protection index: S401: Verify the effectiveness of access control by simulating authorized and unauthorized access attempts and obtain an access control score. Specifically, you can use penetration testing tools (such as Burp Suite) to perform SQL injection, cross-site scripting attacks, etc.

[0085] A specific scoring method can be: (1) If the system can effectively identify and block all unauthorized access, the access control score is a full 10 points; (2) If the system can block unauthorized access in most cases (70% of access), but there are a few loopholes, the access control score is 7 points; (3) If the system is weak in blocking unauthorized access (30% of access), the access control score is 3 points; and so on.

[0086] S402: Use an encryption algorithm assessment tool (such as openssl) to test the strength of the encryption algorithm used by the database to obtain an encryption algorithm strength score.

[0087] A specific scoring method can be: (1) If a recommended high-strength encryption algorithm is used, the encryption algorithm strength score is a full 10 points; (2) If an appropriate encryption algorithm is used but does not fully meet the highest security standards, the encryption algorithm strength score is 6 points; (3) If the encryption algorithm is outdated or has known weaknesses, the encryption algorithm strength score is 2 points; (4) If a very weak encryption algorithm is rarely used or used, the encryption algorithm strength score is 0 points.

[0088] S403: Evaluate the security of each process in the key lifecycle management and obtain a key management mechanism score.

[0089] A specific scoring method can be: (1) If key management follows best security practices, the key management mechanism is scored a full 10 points; (2) If key management is good but there is room for improvement, the key management mechanism is scored a 6 point; (3) If key management has significant flaws, the key management mechanism is scored a 3 point; (4) If key management is extremely insecure or almost unmanaged, the key management mechanism is scored a 0 point.

[0090] S404: Use a data recovery tool (such as R-Studio) to recover data from the encrypted storage, check whether the data can be illegally recovered, and obtain an encrypted storage integrity score.

[0091] A specific scoring method can be: (1) If the encrypted data cannot be recovered at all, the encryption storage integrity score is a full score of 10 points; (2) If the encrypted data can be partially recovered but requires complex methods, the encryption storage integrity score is 5 points; (3) If the encrypted data is relatively easy to recover, the encryption storage integrity score is 1 point; (4) If the encrypted data is completely recoverable, the encryption storage integrity score is 0 points.

[0092] S405: Perform weighted calculation on the access control score, encryption algorithm strength score, key management mechanism score, and encryption storage integrity score to obtain evaluation data A3 of the database encryption protection index.

[0093] This scoring method ensures a comprehensive assessment of database security by comprehensively considering access control, encryption algorithm strength, key management, and the integrity of encrypted storage, helping to identify and strengthen potential security vulnerabilities.

[0094] Specifically, the evaluation content of the operation log recording indicator is: design and implement a detailed operation log recording plan to record the time, personnel, operation content and other information of all key operations.

[0095] like Figure 5 As shown in the flowchart of obtaining the evaluation data of the operation log record index of the present application, the present application provides a specific process for obtaining the evaluation data of the operation log record index: The equipment operation log is obtained by accessing the on-site equipment data. The access methods include but are not limited to scanning QR codes, data interface reading areas, data transfer, etc. After obtaining the operation log, the system automatically identifies the log. The identification method is to first parse the log using multiple parsing methods, judge the encryption method, and then traverse, compare, identify and analyze the system's embedded database tags after parsing to identify whether the transmitted data contains system startup (including startup location), automatic time synchronization time, core sensor installation time, startup test (including test location), modification of calibration parameters, data export and viewing, operation and management account login, log file generation time, system self-test information, system abnormal alarm information and other information to check log integrity; automatically identify and compare log file timestamps to further check log timestamp errors.

[0096] S501: Use a log analysis tool (such as ELK Stack) to analyze the collected logs to check whether there are any missing key event records and obtain a log integrity score.

[0097] A specific scoring method can be: (1) If no key operation records are missing, the log integrity score is a full 10 points; (2) If very few key operation records are missing, the log integrity score is 7 points; (3) If a large number of key operation records are missing, the log integrity score is 3 points; (4) If a large number of key operation records are missing, the log integrity score is 0 points.

[0098] S502: Use a timestamp verification tool (such as Log2timeline) to verify the timestamp in the log, evaluate the consistency between the timestamp recorded in the log and the actual operation time, and obtain a timestamp accuracy score.

[0099] A specific scoring method can be: (1) If all timestamps are completely accurate, the timestamp accuracy score is a full 10 points; (2) If most timestamps are accurate but a few are biased, the timestamp accuracy score is 6 points; (3) If many timestamps are significantly biased, the timestamp accuracy score is 2 points; (4) If timestamps are generally inaccurate or missing, the timestamp accuracy score is 0 points.

[0100] S503: Use a rights management tool (such as ACLs) to test the access control settings of the log file, evaluate the access rights management of the log file, and obtain an access rights control score.

[0101] A specific scoring method can be: (1) If all unauthorized access is successfully blocked, the access control score is a full 10 points; (2) If most unauthorized access is blocked and a few are successful, the access control score is 5 points; (3) If multiple unauthorized access is successful, the access control score is 1 point; (4) If almost all unauthorized access is successful, the access control score is 0 points.

[0102] S504: Perform weighted calculation on the log integrity score, timestamp accuracy score, and access permission control score to obtain evaluation data A4 of the operation log record index.

[0103] This scoring method takes into account the integrity of the log, the accuracy of the timestamp, and the control of access rights. This method can comprehensively evaluate the performance of the log system and help identify and strengthen potential security vulnerabilities in the monitoring system.

[0104] Specifically, the content of the environmental field monitoring equipment data compliance assessment is: inputting the monitoring log and the monitoring process data into a pre-established environmental field monitoring equipment data compliance evaluation model to obtain a data-assisted compliance evaluation result.

[0105] like Figure 6 As shown in the flowchart of the environmental field test data compliance assessment obtained in this application, this application provides a specific procedure for the environmental field data compliance assessment based on the convolutional neural network model: S601: Inputting environmental field monitoring data, log data, etc. into an environmental field monitoring equipment data compliance evaluation model by automatically reading. The environmental field monitoring equipment data compliance evaluation model is a neural network model.

[0106] S602: The environmental field monitoring equipment data compliance evaluation model automatically gives the data-assisted compliance evaluation result A7 (full score 100 points).

[0107] A specific scoring method can be: According to the specific test project indicators, the total amount of data entering the model is M, and the rationality of the process parameters of the data is judged. The amount of non-compliant data is N, and the scoring result is: (1-N / M)*100.

[0108] Furthermore, based on the above embodiments, the multidimensional quantitative evaluation index set in the method provided by the present application may also include: key parameter modification and calibration deviation limit indicators, data collection and transmission completeness, timeliness and accuracy indicators, and original record printing anti-counterfeiting indicators.

[0109] The evaluation contents of key parameter modification and calibration deviation limit indicators include: classification and disposal of key parameters of monitoring equipment, setting modification or adjustment permissions and read-only recording functions for configurable adjustment parameters (flue cross-sectional area, baseline oxygen content, Parshall trough model, light source signal strength, etc.) to limit unauthorized modification; setting modification range limits for correction parameters (slope, intercept, etc.) of direct-output data, and the impact on the data should be within the prescribed permitted range as much as possible; such parameters can also be canceled or completely sealed to prevent arbitrary modification of data.

[0110] The coefficients (correction factors, zero point or span calibration coefficients, etc.) generated each time the monitoring equipment is calibrated should be compared with the initial and last calibration coefficients to calculate the deviation rate. Set reasonable calibration deviation limits, and calibrations that exceed the limits will not be allowed to pass, and the equipment will output an alarm state. The coefficients (correction factors, zero point or span calibration coefficients) generated by calibration cannot be modified again to ensure the accuracy and reliability of the monitoring data.

[0111] The obtained permission control score, calibration deviation control score, read-only record and anti-tampering score are combined for calculation to obtain the evaluation data of key parameter modification and calibration deviation limit indicators.

[0112] Among them, use device management software (for example: configuration software provided by the device manufacturer) to try to modify key parameters, check whether the system correctly allows or denies modifications based on user permissions, and obtain permission control scores.

[0113] Perform simulated calibration on the equipment, calculate the deviation between the calibration parameters (such as correction factor, zero point or span calibration coefficient) and the standard value, and obtain the calibration deviation control score.

[0114] Attempt to modify recorded calibration parameters to check if the system effectively prevents modification and logs the attempt, obtaining a read-only log and tamper-proof score.

[0115] By evaluating the effectiveness of access controls, calibration deviations, and anti-tampering measures, this approach can comprehensively evaluate the performance of equipment in terms of operational security and data accuracy, helping to identify and strengthen potential security gaps in monitoring systems.

[0116] The evaluation contents of the indicators of completeness, timeliness and accuracy of data collection and transmission include: evaluating the completeness, timeliness and accuracy of the data, parameters and status collection of monitoring equipment, evaluating the security of equipment in the networking process, and preventing network attacks and data leakage. Evaluate the security of sensors and data collection lines, check the signal isolation measures of data transmission lines, whether there are signal attenuation components installed, whether there is remote control possibility, whether the data communication protocol is safe and cannot be tampered with and the parameters are correctly configured, whether the signal anti-interference measures are complete, whether the target address of the transmission is correct, etc. Use redundant data transmission and verification mechanisms, such as digital signatures and hash verification, to ensure that the data transmission process is not interrupted or tampered with.

[0117] The obtained data integrity score, data real-time score and data security score are combined for calculation to obtain evaluation data of the completeness, timeliness and accuracy indicators of data collection and transmission.

[0118] Among them, use data acquisition test tools (such as NI data acquisition cards) to simulate real data acquisition scenarios and verify the integrity of data; use digital signatures or hash checks (such as using OpenSSL) to verify the integrity of data during transmission; and obtain data integrity scores.

[0119] Use network testing tools (such as Iperf) to test data transmission rate and latency and obtain data real-time scores.

[0120] Use network intrusion detection tools (such as Snort) to monitor network traffic, detect potential network attacks, and obtain data security scores.

[0121] By simultaneously considering the integrity, real-time and security of data, this method can comprehensively evaluate the performance of monitoring equipment on key performance indicators, helping to identify and enhance potential weaknesses in the monitoring system.

[0122] The evaluation contents of the anti-counterfeiting index of original record printing include: designing and implementing the anti-counterfeiting scheme for original record printing; determining the unique ID information of the two when the monitoring equipment is connected to the printer, and displaying the information on the printed record; using the dynamic encryption algorithm + XOR data encryption + cyclic redundancy check to form an encryption code when printing out the original data; automatically generating an anti-counterfeiting code (such as a QR code or a bar code) when printing the original data to generate a printed report; including the use of anti-counterfeiting paper, special ink, watermark, QR code and other technical means to ensure the authenticity and non-tamperability of the printed records.

[0123] The obtained encryption algorithm and data security score, anti-counterfeiting code authenticity and integrity score, and physical anti-counterfeiting measures score are combined for calculation to obtain evaluation data of the original record printing anti-counterfeiting index.

[0124] Use a QR code scanning tool (e.g. QR Code Scanner) to verify the security code on the printed record, check whether the record details can be displayed correctly and confirm the authenticity, and obtain the authenticity and integrity score of the security code.

[0125] Use ink detection tools such as UV lights to detect special inks, verify the use of watermarks and security papers, and obtain scores for physical security measures.

[0126] By evaluating the strength of technical encryption, the authenticity of anti-counterfeiting codes, and the comprehensive application of physical anti-counterfeiting technology, it helps to ensure the authenticity and security of printed records, prevent data tampering and forgery, and enhance the trust and reliability of the entire monitoring system.

[0127] During the implementation process, the functions of the equipment are tested one by one according to the quantitative indicator system, and the test results are recorded. By simulating the real environment, the accuracy and security of the data collection, transmission, storage and printing processes are verified. The test results are statistically analyzed and scored according to the preset quantitative indicator calculation formula to evaluate whether the data security (anti-counterfeiting) function of the environmental field monitoring equipment meets the requirements.

[0128] Furthermore, according to the scoring results, environmental field monitoring equipment can be divided into three safety levels: A, B, and C. Among them, 90 points (taking the full score as 100 points) and above correspond to safety level A (high safety level), 80-90 points correspond to safety level B (medium safety level), and below 80 points correspond to safety level C (low safety level).

[0129] Based on the evaluation and verification results, an evaluation report is formed and improvement and applicable suggestions are put forward to provide a basis for the production and manufacturing of monitoring equipment manufacturers, the selection of monitoring equipment by environmental monitoring agencies or pollutant discharge units, and the supervision and management of government management departments.

[0130] In summary, this application ensures the security of monitoring equipment in data collection, transmission, storage and other links through comprehensive data security assessment and verification, and prevents data from being tampered with or forged. By establishing a multi-dimensional quantitative evaluation index system, the data security function of on-site monitoring equipment is comprehensively evaluated, and the data security level of monitoring equipment is given. Technical guidance is provided for equipment manufacturing and function optimization, and the authenticity and accuracy of monitoring data are improved. It provides a plan basis for equipment selection by environmental monitoring units and supervision and management by government management departments, and improves the public's trust in monitoring data.

[0131] In addition, the present application also provides a data security protection verification system for pollution source on-site monitoring equipment, such as Figure 7 As shown in the structural block diagram of the data security protection verification system for pollution source on-site monitoring equipment provided in the present application, the system specifically includes: a memory 71 and a processor 72, wherein the memory 71 stores a computer program, and when the computer program is executed by the processor 72, it implements any of the above-mentioned data security protection verification methods for pollution source on-site monitoring equipment.

[0132] It can be understood that the specific implementation process of the data security protection verification system for pollution source on-site monitoring equipment provided in this application corresponds to the above method and will not be repeated here.

[0133] The professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0134] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0135] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A data security protection verification method for environmental field monitoring equipment, characterized in that: include: Integrate high-precision positioning and time synchronization equipment on the environmental field monitoring equipment in advance, use the high-precision positioning and time synchronization equipment to automatically collect time and positioning information of the environmental field monitoring equipment, calculate positioning error and time error respectively, and obtain evaluation data of positioning and timing accuracy indicators; Conduct penetration tests on the interfaces and storage media of environmental field monitoring equipment to verify whether the interfaces and storage media can identify destructive behaviors and obtain evaluation data on the sealing security indicators of the interfaces and storage media; Use security scanning tools and encryption analysis tools to verify the encryption strength and access control mechanism of the database in the environmental field monitoring equipment, and obtain evaluation data of the database encryption protection index; Simulate operation scenarios to operate equipment, evaluate whether environmental field monitoring equipment fully records operation logs, and obtain evaluation data on operation log recording indicators; Based on the evaluation data of each indicator in the multidimensional quantitative evaluation indicator set, a data security quantitative evaluation result of the environmental field monitoring equipment is generated. The multidimensional quantitative evaluation indicator set includes at least: positioning and timing accuracy indicators, interface and storage medium sealing security indicators, database encryption protection indicators and operation log recording indicators.

2. The data security protection verification method according to claim 1, characterized in that: Also includes: Obtain monitoring logs and monitoring process data of environmental field monitoring equipment in real time during operation; Input the monitoring log and monitoring process data into a pre-established environmental field monitoring equipment data compliance evaluation model to obtain a data-assisted compliance evaluation result, and automatically generate a data security protection verification report in combination with the data security quantitative evaluation result of the environmental field monitoring equipment for real-time feedback; Among them, the environmental field monitoring equipment data compliance evaluation model is a neural network model that adopts an association rule mining model. The environmental field monitoring equipment data compliance evaluation model is pre-trained using monitoring training data with different emission characteristics, emission factors and monitoring methods with labels to predict whether the input data complies with the environmental monitoring compliance standards.

3. The data security protection verification method according to claim 2, characterized in that: The neural network model adopts a multi-layer deep convolutional neural network architecture, including an input layer, two hidden layers, and an output layer; Among them, the preprocessed raw data is extracted through features, and the vectorized features become the input layer; the hidden layer adopts the CNN convolutional neural network mode, which is divided into two layers: the convolution layer and the pooling layer; the convolution layer is responsible for performing convolution operations on the data of the input layer, and convolution calculations are performed with the input data through sliding convolution kernels, and feature information in the input data is extracted during the convolution process; the filter of the convolution layer selects a filter of size 128; the pooling layer downsamples the output of the convolution layer, and the pooling method is bit average pooling, the average value of all values ​​in the area; the output layer performs forward calculations, and the calculation results are obtained by passing the raw data through weight calculations and activation function constraints to the output layer; the Relu function is selected as the activation function.

4. The data security protection verification method according to claim 2, characterized in that: The multi-dimensional quantitative evaluation index set also includes: key parameter modification and calibration deviation limit index, data collection and transmission completeness and timeliness accuracy index, and original record printing anti-counterfeiting index; Combine the obtained device permission management score, calibration deviation control score, and read-only record and anti-tampering score to calculate and obtain the evaluation data of key parameter modification and calibration deviation limit indicators; Combine the acquired data integrity score, data real-time score, and data security score to calculate and obtain evaluation data on the completeness, timeliness, and accuracy of data collection and transmission; The obtained encryption algorithm and data security score, anti-counterfeiting code authenticity and integrity score, and physical anti-counterfeiting measures score are combined for calculation to obtain evaluation data of the original record printing anti-counterfeiting index.

5. The data security protection verification method according to claim 4 is characterized in that: Based on the evaluation data of each indicator in the multi-dimensional quantitative evaluation indicator set, the data security quantitative evaluation results of the environmental field monitoring equipment are generated, including: Perform weighted calculation on each indicator in the multi-dimensional quantitative evaluation indicator set to generate the data security quantitative evaluation results of the environmental field monitoring equipment; The weight of each indicator is determined by the following method: The results are obtained comprehensively based on the different impacts of different assessment data on on-site detection of pollution sources.

6. The data security protection verification method according to any one of claims 1 to 5, characterized in that: The evaluation data for obtaining positioning and timing accuracy indicators includes: Use high-precision time synchronization equipment to test the time error between the time displayed by the on-site monitoring equipment in the environment and the standard time, and obtain a time accuracy score based on the time error; Using a high-precision positioning synchronization device to measure the actual position of the environmental field monitoring device, calculating the position error between the actual position and the reported position, and obtaining a position accuracy score based on the position error; Use a simulator to simulate different degrees of satellite signal obstruction or interference, record whether an alarm is generated and whether the event is accurately recorded in the case of satellite signal obstruction or interference, and obtain an anti-interference ability score based on the recording results; Use penetration testing tools to monitor network traffic and obtain security scores based on whether modified interfaces or instructions are found; The time accuracy score, position accuracy score, anti-interference ability score and safety score are weightedly calculated to obtain evaluation data A1 of the positioning and timing accuracy index.

7. The data security protection verification method according to any one of claims 1 to 5, characterized in that: The evaluation data of the acquisition interface and storage medium sealing safety index includes: Identify all communication interfaces of environmental field monitoring equipment, including wired and wireless interfaces; Use data monitoring tools to connect between field monitoring equipment and data acquisition terminals or platforms to monitor the data transmission process; Through non-periodic data acquisition methods, query instructions are actively sent to on-site monitoring equipment to obtain real-time monitoring data; Use analysis tools to parse the monitoring data frames and automatically identify whether the data encryption method is plain text, frame data encryption, or byte-by-byte encryption; Automatically identify the monitored data, compare the data with the system's built-in database, and identify the integrity of the data; Use monitoring tools to simulate data delivery and perform a second read to verify whether the data can be tampered with; Based on whether the data can be monitored, the encryption method of the data, the integrity of the data, and whether the data has been tampered with, the evaluation data A2 of the sealing security index of the interface and the storage medium is obtained.

8. The data security protection verification method according to any one of claims 1 to 5, characterized in that: The evaluation data of obtaining the database encryption protection index includes: By simulating authorized and unauthorized access attempts, the effectiveness of access control is verified and access control scores are obtained; Use encryption algorithm assessment tools to test the strength of the encryption algorithm used in the database and obtain the encryption algorithm strength score; Evaluate the security of each process in the key lifecycle management and obtain the key management mechanism score; Use data recovery tools to recover data from encrypted storage, check whether the data can be illegally recovered, and obtain the encrypted storage integrity score; The access control score, encryption algorithm strength score, key management mechanism score and encryption storage integrity score are weightedly calculated to obtain evaluation data A3 of the database encryption protection index.

9. The data security protection verification method according to any one of claims 1 to 5, characterized in that: The evaluation data of obtaining the equipment operation log record index includes: Use log analysis tools to analyze the collected logs to check whether there are any missing key event records and obtain the log integrity score; Use the timestamp verification tool to verify the timestamp in the log, evaluate the consistency between the timestamp recorded in the log and the actual operation time, and obtain the timestamp accuracy score; Use permission management tools to test the access control settings of log files, evaluate the access permission management of log files, and obtain access permission control scores; The log integrity score, timestamp accuracy score and access permission control score are weightedly calculated to obtain evaluation data A4 of the operation log record index.

10. A data security protection verification system for environmental field monitoring equipment, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the data security protection verification method for environmental field monitoring equipment according to any one of claims 1 to 9 is implemented.

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