FCT safe collaborative scheduling method and system based on full-link data fusion

Abstract

The present invention discloses an FCT security collaborative scheduling method and system based on full-link data fusion, which relates to the field of data processing and cloud computing technology. The method includes: a cloud data platform parses the items and instruction sequences of the production test plan; sends them to the FCT client to match the execution engine sequence; tests the production test items in sequence, verifies the security of the signal benchmark through encryption verification, and generates results; if it passes, stores the log and executes the next item; if it fails, stores the log, analyzes the window backtracking data to determine the abnormality, and schedules the execution engine. The present invention solves the technical problems of insufficient management isolation in factory data scheduling, lack of a security collaborative protection mechanism for signal benchmarks during the production test process, easy tampering, data mixing, and substandard security. It achieves the technical effect of using a security collaborative mechanism to realize differentiated management of factory data and tampering prevention of signal benchmarks, combined with full-link data scheduling to optimize the test process, and improve data security, management isolation, and corporate compliance.

Description

Technical Field

[0001] The present invention relates to the field of data processing and cloud computing technology, and in particular to a FCT secure collaborative scheduling method and system based on full-link data fusion. Background Art

[0002] In the production process of electronic products, FCT (final component testing) is crucial to ensuring product quality, and its data accuracy and security directly affect production efficiency and corporate compliance. In existing technologies, FCT testing mainly relies on decentralized tool systems, using local storage of test data, manual configuration of test processes, and extensive permission management methods. These methods can basically meet the needs in a single factory or simple testing scenario, but they expose significant limitations when faced with complex scenarios such as multi-factory collaborative production and high-security level testing: data from different factories are mixed and stored, lacking an effective isolation mechanism, resulting in chaotic management and difficult traceability; the signal reference used to determine product qualification during the production test process lacks encryption protection and is easily tampered with; data storage and transmission security measures are insufficient, making it difficult to meet corporate compliance requirements for privacy protection and permission control. Summary of the Invention

[0003] This application provides an FCT secure collaborative scheduling method and system based on full-link data fusion, which is used to solve the technical problems of insufficient management isolation in factory data scheduling, lack of a secure collaborative protection mechanism for signal benchmarks during production testing, easy tampering, data mixing, and substandard security.

[0004] The first aspect of the present application provides an FCT security collaborative scheduling method based on full-link data fusion, the method comprising: using a cloud data platform to perform production test plan analysis according to the target test product and test requirements, and obtain a production test item sequence and a production test instruction sequence; sending the production test item sequence and the production test instruction sequence to the FCT client, using the FCT client to perform responsible chain execution engine matching to obtain an execution engine sequence; based on the execution engine sequence, testing the production test item sequence according to the production test instruction sequence in sequence, and performing security verification on the corresponding signal benchmark during the test through the full-link staggered collaborative encryption verification strategy. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result; when the test verification result is that the production test item passes, the test log is uploaded to the cloud data platform for partitioned storage, and the test of the next production test item is executed in order from the front to the back of the production test item sequence until all production test items are tested; when the test verification result is that the production test item fails, the test log is uploaded to the cloud data platform for partitioned storage, and the data stored in the window backtracking unit in the cloud data platform is simultaneously extracted for time series iteration recording. Memory analysis is performed to determine abnormal information, and the corresponding execution engine is securely collaboratively scheduled based on the abnormal information; wherein, when the production test item sequence is tested in turn according to the production test instruction sequence based on the execution engine sequence, the corresponding signal benchmark is security verified through the full-link staggered collaborative encryption verification strategy. If the verification is successful, the test verification is performed based on the verified signal benchmark to generate a test verification result, including: extracting the signal benchmark staggered encryption update constraint and the collaborative encryption asynchronous constraint in the full-link staggered collaborative encryption verification strategy; obtaining the usage log sequence set of the full-link signal benchmark of the production test item sequence, analyzing the minimum value from the timestamp of the usage log in the usage log sequence set to the current time window, and judging whether the minimum value meets the signal benchmark staggered encryption update constraint; if so, security verification is performed based on the encryption key of the signal benchmark; if not, triggering the signal benchmark staggered encryption update instruction, and according to the signal benchmark staggered encryption update instruction, combining the usage log sequence set and the collaborative encryption asynchronous constraint to update the encryption key of the signal benchmark, and performing security verification on the signal benchmark based on the updated updated encryption key.

[0005] Preferably, according to the signal benchmark staggered encryption update instruction, the encryption key of the signal benchmark is updated in combination with the use of a log sequence set and the collaborative encryption asynchronous constraint, and the security of the signal benchmark is verified based on the updated updated encryption key, including: extracting the encryption key in the use log sequence set to obtain a historical encryption key set; using a random number generator to generate an initial encryption key; using a key security loss function to perform a loss analysis on the initial encryption key and the historical encryption key set to determine the initial loss amount; judging whether the initial loss amount meets the collaborative encryption asynchronous constraint, if not, performing a direction adjustment on the initial encryption key to obtain an adjusted encryption key; when the adjusted encryption key meets the collaborative encryption asynchronous constraint, using a key encryption key to encrypt the adjusted encryption key to obtain an updated updated encryption key.

[0006] Preferably, the key security loss function is:

[0007] ;

[0008] in, is the initial loss amount, is the initial encryption key, n is the total number of historical encryption keys in the historical encryption key set and is a positive integer, is the i-th historical encryption key in the historical encryption key set.

[0009] Preferably, it is determined whether the initial loss amount satisfies the collaborative encryption asynchronous constraint; if not, the initial encryption key is directionally adjusted to obtain the adjusted encryption key, including: determining whether the initial loss amount is greater than or equal to the loss amount threshold in the collaborative encryption asynchronous constraint; if not, determining the directional adjustment bandwidth based on the size of the initial loss amount; taking the adjustment direction away from the historical encryption key set, randomly adjusting the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key.

[0010] Preferably, the adjustment direction is to move away from the historical encryption key set, and the initial encryption key is randomly adjusted based on the directional adjustment bandwidth to obtain the adjusted encryption key, including: randomly adjusting the initial encryption key according to the directional adjustment bandwidth to obtain multiple stage-adjusted encryption keys; using the key security loss function, traversing and calculating the loss amounts of the multiple stage-adjusted encryption keys and the historical encryption key set to obtain multiple stage-adjusted loss amounts; extracting the stage-adjusted encryption key corresponding to the maximum value from the multiple stage-adjusted loss amounts, and using it as the directional stage-adjusted encryption key; updating the directional stage-adjusted encryption key to the adjustment direction, and adjusting the remaining multiple stage-adjusted encryption keys according to the directional adjustment bandwidth to obtain multiple follow-up adjustment encryption keys; updating the directional stage-adjusted encryption key based on the multiple follow-up adjustment encryption keys until the preset number of updates is met to obtain the adjusted encryption key.

[0011] Preferably, the method includes: updating the directional stage adjustment encryption key based on the multiple follow-up adjustment encryption keys until a preset number of updates is met to obtain the adjustment encryption key, including: using a key security loss function to calculate the loss amount of the multiple follow-up adjustment encryption keys and the historical encryption key set to obtain multiple follow-up adjustment loss amounts; when there is a follow-up adjustment loss amount among the multiple follow-up adjustment loss amounts that is greater than or equal to the stage adjustment loss amount corresponding to the directional stage adjustment encryption key, updating the follow-up adjustment encryption key corresponding to the maximum value of the multiple follow-up adjustment loss amounts to the directional stage adjustment encryption key, and again based on the directional stage adjustment encryption key, directionally adjusting the remaining multiple follow-up adjustment encryption keys according to the directional adjustment bandwidth until the preset number of updates is met to obtain the adjustment encryption key.

[0012] Preferably, a historical test log sequence is extracted from the window backtracking unit; association words are extracted from the first historical test log in the historical test log sequence according to a preset abnormal association word set, and the extraction result is added to an empty vector to obtain a first memory; association words are extracted from the second historical test log in the historical test log sequence based on the preset abnormal association words and the first memory, and the first memory is updated according to the extraction result to obtain a second memory; and so on, until the end of the historical test log sequence is reached, and a time series iterative memory is obtained; the time series iterative memory is identified using an abnormal information extractor to obtain abnormal information.

[0013] Preferably, the first historical test log in the historical test log sequence is subjected to associated word extraction according to a preset abnormal associated word set, and the extraction result is added to an empty vector to obtain a first memory, including: decomposing the first historical test log according to the word level to generate a word sequence set; traversing the word sequence set and matching it with the preset abnormal associated word set for semantic similarity, and if the match is successful, adding it as an associated word to the empty vector to obtain the first memory.

[0014] The second aspect of the present application provides an FCT security collaborative scheduling system based on full-link data fusion, the system comprising: a production test sequence acquisition module for using a cloud data platform to perform production test plan analysis according to the target test product and test requirements, and obtain a production test item sequence and a production test instruction sequence; an engine sequence acquisition module for sending the production test item sequence and the production test instruction sequence to the FCT client, using the FCT client to perform responsible chain execution engine matching to obtain an execution engine sequence; a test result generation module for testing the production test item sequence according to the production test instruction sequence in turn based on the execution engine sequence, and in the test, the corresponding signal is encrypted and verified through a full-link staggered collaborative encryption strategy. The security of the benchmark is verified. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result; the test execution module is used to upload the test log to the cloud data platform for partitioned storage when the test verification result is that the production test item has passed, and execute the test of the next production test item in order from front to back according to the production test item sequence until all production test items are tested; the collaborative scheduling execution module is used to upload the test log to the cloud data platform for partitioned storage when the test verification result is that the production test item has failed, and simultaneously perform time-series iterative memory analysis on the data stored in the window backtracking unit extracted from the cloud data platform, determine the abnormal information, and perform security collaborative scheduling on the corresponding execution engine based on the abnormal information.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] This application parses the production test plan through the cloud data platform, obtains the production test item sequence and the production test instruction sequence and sends them to the FCT client, obtains the execution engine sequence through the responsible chain execution engine matching, and verifies the security of the signal benchmark through the full-link staggered collaborative encryption verification strategy when executing the test based on the sequence. Based on the test verification results, the log is partitioned and stored or the time series iterative memory analysis is performed to determine the abnormal information and schedule the execution engine, thereby realizing the safe collaborative scheduling of the FCT test process and the full-link integrated management of data, reducing the risk of signal benchmark tampering during the production test process, and significantly improving data isolation and test result accuracy. It achieves the technical effect of using a secure collaborative mechanism to realize differentiated management of factory data and tampering prevention of signal benchmarks, combining full-link data scheduling to optimize the test process, and improving data security, management isolation and enterprise compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of the FCT secure collaborative scheduling method based on full-link data fusion provided in an embodiment of the present application.

[0019] Figure 2 It is a structural diagram of the FCT safe collaborative scheduling system based on full-link data fusion provided in an embodiment of the present application.

[0020] Description of the accompanying drawings: production test sequence acquisition module 1, engine sequence acquisition module 2, test result generation module 3, test execution module 4, collaborative scheduling execution module 5. DETAILED DESCRIPTION

[0021] This application provides an FCT secure collaborative scheduling method and system based on full-link data fusion, which is used to solve the technical problems of insufficient management isolation in factory data scheduling, lack of a secure collaborative protection mechanism for signal benchmarks during production testing, easy tampering, data mixing, and substandard security.

[0022] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1, as Figure 1 As shown, the FCT secure collaborative scheduling method based on full-link data fusion includes:

[0025] Step A1000: Utilize the cloud data platform to analyze the production test plan based on the target test product and test requirements, and obtain the production test item sequence and production test instruction sequence.

[0026] In this embodiment, the production test plan is a comprehensive test plan developed based on the target product and its test requirements. The production test item sequence is a sequence of test item types obtained by the cloud data platform after parsing the production test plan. It is used to clearly define the various test items to be executed during the production test process and their order. The production test instruction sequence is a set of instructions that corresponds one-to-one with the production test item sequence and is used to guide the specific execution of each production test item.

[0027] Specifically, technical personnel first input the target product's model, specifications, and specific test requirements (such as functional testing and performance stability testing). The cloud-based data platform then structures this information using a built-in parsing engine. Based on pre-set industry-standard templates or customized company rules, the parsing engine breaks down these abstract test requirements into a sequence of executable production test items. For example, if the target product is a certain power adapter model and the test requirement is to verify its output performance, the parsing process can generate production test items such as input voltage stability testing, output current overload protection testing, and ripple factor testing. These test items are then arranged in a logical order to form a sequence.

[0028] After obtaining the production test item sequence, the cloud data platform retrieves the corresponding production test instructions from the underlying database based on the mapping relationship between the production test items and the production test instructions. The production test instructions include test limits (such as the allowable input voltage fluctuation range is 190V-240V), signal reference data (such as the standard output voltage is 12VDC), and specific execution modules (such as calling the voltage detection module numbered FCT-032). Taking the input voltage stability test as an example, the corresponding production test instruction sets the test limit as ≤±0.5V when the grid voltage is within the range of 190V-240V; the signal reference data is the rated input voltage of 220VAC and the rated output voltage of 12VDC; and the execution module is a dedicated voltage acquisition module integrated in the FCT client. The platform uses a data verification mechanism to ensure that each production test item corresponds to the production test instruction one by one to avoid missing or mismatched instructions.

[0029] Through the cloud data platform's automated parsing engine, standardized data mapping mechanism, and centralized database management, accurate conversion from test requirements to executable production test sequences is achieved, thereby improving the efficiency of production test plan formulation, reducing human errors, and enhancing the maintainability of test standards.

[0030] Step A2000: Send the production test item sequence and the production test instruction sequence to the FCT client, use the FCT client to perform responsible chain execution engine matching, and obtain an execution engine sequence.

[0031] In this embodiment, the FCT client is a terminal that receives a sequence of production test items and instructions from a cloud data platform and executes the responsible chain execution engine matching. The execution engine sequence is an ordered list of modules generated by the responsible chain execution engine matching to guide test execution.

[0032] Optionally, after the production test item sequence and production test instruction sequence are sent to the FCT client, the FCT client first receives the sequence data transmitted by the cloud data platform through a network communication module (such as HTTP / HTTPS protocol) and verifies the data integrity (such as by comparing MD5 hash values to ensure that the data has not been tampered with). After the verification is passed, the client initiates the responsible chain execution engine matching mechanism: it generates a unique responsibility identifier for each production test item (such as a combination identifier containing the production test item number, timestamp, and device ID) and binds the responsibility identifier to the execution module based on the corresponding relationship between the execution module in the production test instruction sequence (such as the voltage detection module numbered FCT-032) and the production test item.

[0033] The execution engine matching module then connects the responsibility identifiers and execution modules in series, following the logical order of the production test sequence (e.g., input voltage test first, then output current test), to form an execution engine sequence. For example, if the production test sequence is input voltage stability test → output current overload protection test → ripple coefficient test, the execution engine sequence will sequentially call the voltage detection module, current protection module, and ripple analysis module, assigning a corresponding responsibility identifier to each module to ensure that the next module is automatically triggered after the previous module completes.

[0034] Through the FCT client's responsible chain execution engine matching mechanism, abstract production test item sequences and instruction sequences are converted into automatically executed engine sequences, achieving the effect of improving the degree of automation of the test process, ensuring the accuracy of task execution sequence, and reducing the cost of human intervention.

[0035] Step A3000: Based on the execution engine sequence, the production test item sequence is tested in sequence according to the production test instruction sequence. During the test, the corresponding signal benchmark is security verified through the full-link staggered collaborative encryption verification strategy. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result.

[0036] In the embodiments of the present application, the full-link staggered collaborative encryption verification strategy is a strategy for verifying the security of the signal reference during testing. The signal reference is a key reference signal value or signal feature set used to determine whether the response of the device under test meets the expected requirements during production testing.

[0037] In one embodiment of the present application, the FCT client first triggers the corresponding execution modules (such as the voltage detection module and the current protection module) according to the execution engine sequence. After each execution module is activated, it retrieves the test limits (such as the input voltage fluctuation range) and signal reference data (such as the standard output voltage value) that match the current production test item from the production test instruction sequence. Based on these instructions, it performs real-time data acquisition on the device under test. For example, during an input voltage stability test, the corresponding execution module continuously collects the device's input voltage value and dynamically compares it with the signal reference and test limits.

[0038] Then, during the test process, the relevant constraints in the full-link staggered collaborative encryption verification strategy are extracted, and the signal benchmark usage log timestamp is analyzed. If the constraints are not met, the encryption instruction is triggered. The encryption key is updated in combination with the constraints to verify the security of the signal benchmark. The specific steps are detailed in A3100-A3200.

[0039] If the security of the signal benchmark is successfully verified through the full-link staggered collaborative encryption verification strategy, the FCT client, based on the verified signal benchmark, drives the execution engine sequence to sequentially call the test limits and execution modules in the production test instruction sequence to perform real-time test verification on the production test item sequence. The specific process is as follows: the execution module collects real-time data from the device under test according to the production test instruction (such as the voltage detection module corresponding to the input voltage stability test item), and dynamically compares it with the signal benchmark (such as the rated voltage 220VAC) and the test limit (such as fluctuation ≤±0.5V). If the data is within the limit, the production test item is judged to have passed, otherwise it is judged to have failed. The generated test verification results contain information such as the signal benchmark version number, test time, and data comparison results, and are uploaded to the cloud data platform for partitioned storage, providing a complete data chain for subsequent full-process traceability or anomaly analysis.

[0040] Through the automated driving of the execution engine sequence, the precise matching of production and test instructions, and the full-link traceability mechanism of test results, the production and test efficiency is improved, human errors are reduced, and the authenticity and traceability of data are guaranteed.

[0041] Step A4000: When the test verification result shows that the production test item has passed, the test log is uploaded to the cloud data platform for partition storage, and the test of the next production test item is executed in order from the front to the back of the production test item sequence until all production test items are tested.

[0042] Specifically, when the test verification result indicates that a production test item has passed, the FCT client first uploads the test log (including raw test data, execution logs, and information about failed items) to the cloud data platform via a secure transmission protocol (such as HTTPS). The cloud data platform automatically creates storage partitions based on pre-set partitioning rules (such as factory ID + production test date + device type) and categorizes and stores the logs according to these partitioning rules, ensuring physical isolation of data from different factories and batches. After the upload is complete, the execution engine automatically calls the next item in the production test item sequence, repeating the aforementioned command matching → test execution → result verification process until all production test items have been executed.

[0043] By using the partitioned storage mechanism of the test log and the automated sequential scheduling logic of the execution engine, we can improve data traceability, ensure enterprise data security and compliance, and optimize production and testing efficiency.

[0044] Step A5000: When the test verification result is that the production test item fails, the test log is uploaded to the cloud data platform for partitioned storage, and the data stored in the window backtracking unit in the cloud data platform is simultaneously extracted for time series iterative memory analysis to determine the abnormal information, and the corresponding execution engine is safely and collaboratively scheduled based on the abnormal information.

[0045] In an embodiment of the present application, the window backtracking unit is a functional unit in the cloud data platform for storing historical test log sequences, and supports extracting historical data (such as logs of the previous N test cycles) according to a preset time window or period.

[0046] Specifically, when the test verification result shows that the production test item fails, the FCT client first encrypts the test log containing the original test data, execution log and failure item information, and uploads it to the cloud data platform, and partitions and stores it according to the factory ID + production test batch + equipment type rules.

[0047] At the same time, the system automatically triggers the window backtracking unit of the cloud data platform, extracts the historical test log sequence, extracts the associated words from the logs in the sequence in sequence according to the preset abnormal associated word set, and iteratively updates the memory vector layer by layer to obtain the time series iterative memory at the end of the sequence, and uses the abnormal information extractor to identify and obtain abnormal information. The specific steps are detailed in A5100-A5500.

[0048] Next, based on the exception information, the signal benchmark used for the abnormal test item is subjected to full-link time-delayed encryption verification. If the verification fails, the signal benchmark replacement request is automatically triggered; then the rescheduling method is determined. If the exception is caused by short-term signal interference or cache errors, the current module can be partially re-executed. At the same time, the test limit can be modified according to the actual situation (such as extending parameters such as the response waiting time), or module replacement scheduling can be performed (such as replacing the abnormal A main module with the spare B module). The execution order of modules in the responsibility chain can also be adjusted through path optimization scheduling (such as executing the communication stability test first and then the response test) or an intermediate verification module can be inserted for output transition. If the error is complex and involves multiple modules, manual intervention is used to guide the scheduling, and the tester is notified to review and select the module to reload or suspend execution. The above method can achieve safe and coordinated scheduling of the execution engine to ensure the accuracy and stability of the test process.

[0049] Through the partitioned storage mechanism of test logs, the layer-by-layer feature extraction algorithm of time-series iterative memory, and the dynamic scheduling strategy of the execution engine, rapid location and targeted response of production test anomalies are achieved, thus shortening troubleshooting time, improving the security of the test process, and ensuring the reliability of test data.

[0050] Furthermore, step A3000 in the method provided in the embodiment of the present application includes:

[0051] A3100: Extract the signal reference staggered encryption update constraints and collaborative encryption asynchronous constraints in the full-link staggered collaborative encryption verification strategy.

[0052] A3200: Obtain a usage log sequence set of the full-link signal benchmark of the production test item sequence, analyze the minimum value from the timestamp of the usage log in the usage log sequence set to the current time window, and determine whether the minimum value meets the signal benchmark staggered encryption update constraint; if so, perform security verification based on the encryption key of the signal benchmark; if not, trigger a signal benchmark staggered encryption update instruction, and according to the signal benchmark staggered encryption update instruction, perform an encryption key update on the signal benchmark in combination with the usage log sequence set and the collaborative encryption asynchronous constraint, and perform security verification on the signal benchmark based on the updated updated encryption key.

[0053] Specifically, when testing the production test item sequence based on the production test instruction sequence based on the execution engine sequence, two key constraints are first accurately extracted from the full-link staggered collaborative encryption verification strategy: the first is the signal-reference staggered encryption update constraint, for example, a clear interval threshold for encryption updates (such as 30 minutes) is set, forcing an encryption update operation to be performed every interval; the second is the collaborative encryption asynchrony constraint, namely the loss threshold constraint in the key security loss function. For example, the loss between the newly generated encryption key and the set of historical encryption keys must be greater than or equal to the loss threshold, ensuring a sufficient difference between the new key and the historical key to avoid problems caused by encryption operation synchronization. The combination of these two factors enables two-factor dynamic authentication of execution time and execution path, ensuring the security and dynamic adaptability of signal-referenced encryption verification.

[0054] Next, a set of full-link signal benchmark usage logs for the production test item sequence is obtained. This set fully records detailed information such as the timestamp, associated module ID, historical encryption time, and status of each signal benchmark usage (e.g., specific log entries such as 2024-04-20 10:00:00, module A, and last encryption time 2024-04-20 09:30:00). This provides comprehensive data support for subsequent analysis of the signal benchmark's usage characteristics and encryption status. The timestamp of each usage log in the set is analyzed, and the minimum value from the timestamp to the current time window is calculated (for example, if the current time window is 1 hour, and the timestamp of one usage log is 50 minutes from the current time window, and another log is 400 minutes from the current time window, the minimum value is 40 minutes). A determination is made as to whether this minimum value meets the signal benchmark's staggered encryption update constraint. If so, no encryption key update is required. Security verification is performed based on the signal benchmark's encryption key. If not (e.g., the constraint requires an update every 30 minutes, and the minimum value is 40 minutes), a signal benchmark staggered encryption update instruction is triggered.

[0055] Then, the encryption keys in the log sequence set are extracted to form a historical encryption key set, and the initial encryption key is generated using a random number generator. The initial loss amount of the encryption key and the historical set is analyzed through the key security loss function. If the collaborative encryption asynchronous constraint is not met, the initial encryption key is adjusted until it is met. The adjusted encryption key is encrypted using the key encryption key to obtain the updated encryption key to verify the signal baseline security. The specific steps are described in detail in A3210-A3250.

[0056] By extracting constraint rules, analyzing usage log time characteristics, triggering and executing encryption update instructions, etc., dynamic encryption management and security verification of the signal benchmark are achieved, ensuring the security and reliability of the signal benchmark during the test process and preventing erroneous test verification results due to signal benchmark failure or tampering.

[0057] Furthermore, step A3200 in the method provided in the embodiment of the present application includes:

[0058] A3210: Extract the encryption key from the usage log sequence set to obtain a historical encryption key set.

[0059] A3220: Generates the initial encryption key using a random number generator.

[0060] A3230: Perform loss analysis on the initial encryption key and the historical encryption key set using a key security loss function to determine an initial loss amount.

[0061] A3240: Determine whether the initial loss amount satisfies the collaborative encryption asynchronous constraint; if not, perform directional adjustment on the initial encryption key to obtain the adjusted encryption key.

[0062] A3250: When the adjusted encryption key satisfies the collaborative encryption asynchronous constraint, the adjusted encryption key is encrypted using the key encryption key to obtain an updated updated encryption key.

[0063] In the embodiment of the present application, the loss amount refers to the quantitative value obtained when performing loss analysis on the initial encryption key and the historical encryption key set using the key security loss function, which is used to determine the degree of difference between the initial encryption key and the historical encryption key.

[0064] Optionally, after receiving the signal reference time-delayed encryption update instruction, each usage log is first parsed to extract the encryption key field (e.g., a log entry containing information such as 2024-04-20 10:05:00, key ID: KEY001, module: A). After removing duplicate records, the valid encryption keys (e.g., KEY001, KEY002, etc.) are stored in an ordered set in timestamp order to obtain a historical encryption key set. This set contains the complete version sequence of the historical encryption keys and is used for difference analysis with the newly generated initial encryption key.

[0065] Subsequently, an initial encryption key is generated using a random number generator, and the difference between the initial key and the historical key set is calculated through the key security loss function to obtain the initial loss amount. The specific steps are described in detail in A3231-A3232.

[0066] Next, determine whether the initial loss amount is greater than or equal to the loss amount threshold in the collaborative encryption asynchronous constraint. If not, determine the directional adjustment bandwidth based on the size of the initial loss amount, and randomly adjust the initial encryption key based on the bandwidth in the direction of away from the historical encryption key set to obtain the adjusted encryption key. The specific steps are described in detail in A3241-A3242.

[0067] Finally, when the loss of the adjusted encryption key meets the loss threshold set by the collaborative encryption asynchrony constraint, the adjusted key is encrypted using a key-encryption mechanism (such as the RSA public key encryption algorithm). First, an RSA public-private key pair is generated. The adjusted key is asymmetrically encrypted with the public key to generate the updated encryption key in ciphertext. This encryption process ensures the security of key transmission and storage through mathematical one-way trapdoor function properties (such as the difficulty of factoring large integers). After encryption, the updated encryption key is obtained.

[0068] Through full-process management and control of historical key analysis, dynamic generation and adjustment, mathematical model quantification, and encryption verification, adaptive updating and security enhancement of encryption keys are achieved, preventing illegal tampering of signal benchmarks, improving the security of production and test data, and meeting corporate compliance requirements.

[0069] Furthermore, step A3230 in the method provided in the embodiment of the present application includes:

[0070] A3231: The key security loss function is:

[0071] .

[0072] A3232: Among them, is the initial loss amount, is the initial encryption key, n is the total number of historical encryption keys in the historical encryption key set and is a positive integer, is the i-th historical encryption key in the historical encryption key set.

[0073] Specifically, first, an initial encryption key is generated using a random number generator to ensure that the initial key is random and unpredictable. For example, a cryptographically secure pseudo-random number generator is used to generate a binary sequence that meets the length requirements as the initial encryption key a. Then, key a and each historical encryption key in the historical encryption key set are combined. (i ranges from 1 to n, n is the total number of historical encryption keys and is a positive integer) Substitute into the key security loss function Calculation. Taking n=3 as an example, if the historical encryption key =0.2, =0.3, =0.4, the resulting a=0.6, then calculate each term separately , sum and multiply by , and obtain the initial loss amount Loss. This calculation quantifies the degree of difference between the initial encryption key and the set of historical encryption keys.

[0074] By generating an initial encryption key and using a mathematical function to calculate the difference between it and the set of historical encryption keys (initial loss amount), the foundation is laid for subsequent judgment on whether the collaborative encryption asynchronous constraints are met, thereby ensuring the security and uniqueness of the encryption key after the update. By accurately quantifying the difference between the initial encryption key and the historical key, the effect of improving security during the encryption key update process is achieved.

[0075] Furthermore, step A3240 in the method provided in the embodiment of the present application includes:

[0076] A3241: Determine whether the initial loss amount is greater than or equal to the loss amount threshold in the collaborative encryption asynchronous constraint; if not, determine the directional adjustment bandwidth based on the size of the initial loss amount.

[0077] A3242: Randomly adjust the initial encryption key based on the directional adjustment bandwidth, with the adjustment direction being away from the historical encryption key set, to obtain the adjusted encryption key.

[0078] In the embodiment of the present application, the directional adjustment bandwidth refers to the range value of adjusting the initial encryption key based on the size of the initial loss amount when it is determined that the initial loss amount does not meet the loss amount threshold in the collaborative encryption asynchronous constraint.

[0079] Specifically, a threshold is first determined for the initial loss amount. This is done by comparing the initial loss amount with a loss threshold (e.g., 0.8) preset in the collaborative encryption asynchronous constraint. If the initial loss amount is less than the threshold (e.g., 0.6), the initial encryption key is insufficiently different from the set of historical encryption keys and needs to be adjusted to meet security requirements.

[0080] Next, the directional adjustment bandwidth is determined based on the magnitude of the initial loss, ensuring that the initial loss is greater than or equal to the loss threshold in the constraint. The adjustment bandwidth is negatively correlated with the initial loss: the larger the initial loss, the greater the difference between the current key and the historical key, and the smaller the required adjustment bandwidth, ensuring that the adjusted key significantly deviates from the historical key set. For example, if the initial loss is 0.6 (with a threshold of 0.8), the directional adjustment bandwidth can be set to 0.2 (the specific value is dynamically calculated based on a preset rule). The dynamic calculation of the directional adjustment bandwidth is based on the difference between the initial loss and the loss threshold in the collaborative encryption asynchronous constraint, establishing a quantitative mapping relationship based on preset rules. For example, when the initial loss is less than the threshold (e.g., initial loss is 0.6 and the threshold is 0.8), the preset rule might define bandwidth = threshold - initial loss, in which case the directional adjustment bandwidth is 0.2. Alternatively, a nonlinear rule might be used, such as bandwidth = (threshold - initial loss) × a dynamic coefficient (e.g., 1.5), to increase the bandwidth as the difference increases, providing a quantitative adjustment range for subsequent key adjustments.

[0081] Finally, the initial encryption key is randomly adjusted according to the directional adjustment bandwidth to obtain multiple stage-adjusted encryption keys, the loss amount of each stage-adjusted encryption key and the historical encryption key set is calculated, the stage-adjusted encryption key corresponding to the maximum value is extracted as the directional stage-adjusted encryption key, and it is used as the adjustment direction and the remaining stage-adjusted encryption keys are adjusted according to the bandwidth to obtain the follow-up adjusted encryption key. The directional stage-adjusted encryption key is updated based on the follow-up adjusted encryption key until the preset number of updates is met to obtain the adjusted encryption key. The specific steps are described in detail in A3242-1-3242-5.

[0082] By judging the threshold of the initial loss amount and determining the directional adjustment bandwidth based on the judgment result, quantitative control of the initial encryption key adjustment is achieved, achieving the effect of maximizing the difference between the new and old keys during the key update process and enhancing the security of signal reference encryption.

[0083] Furthermore, step A3242 in the method provided in the embodiment of the present application includes:

[0084] A3242-1: Randomly adjust the initial encryption key according to the directional adjustment bandwidth to obtain multiple stages of adjusted encryption keys.

[0085] A3242-2: Using the key security loss function, traverse and calculate the loss amounts of the multiple-stage adjusted encryption keys and the historical encryption key set to obtain multiple-stage adjusted loss amounts.

[0086] A3242-3: Extract the phase-adjusted encryption key corresponding to the maximum value from the multiple phase-adjusted loss amounts, and use it as the directional phase-adjusted encryption key.

[0087] A3242-4: Update the directional stage-adjusted encryption key to the adjustment direction, adjust the remaining multiple stage-adjusted encryption keys according to the directional adjustment bandwidth, and obtain multiple follow-up adjustment encryption keys.

[0088] A3242-5: Update the directional phase adjusted encryption key based on the multiple follow-up adjusted encryption keys until a preset number of updates is met to obtain the adjusted encryption key.

[0089] Specifically, first, the initial encryption key is randomly adjusted according to the directional adjustment bandwidth, and multiple adjustment values are generated within the bandwidth range through a random number generator (for example, if the directional adjustment bandwidth is 0.3 and the initial encryption key is 0.6, multiple values are randomly generated in the interval [0.6-0.3,0.6+0.3], such as 0.4, 0.7, 0.9, etc.). After superimposing with the initial encryption key, multiple stages of adjusted encryption keys are obtained. The key search space is expanded through randomization processing, and key diversity is increased.

[0090] Then, using the key security loss function , calculate the encryption key and historical encryption key set for each stage. Assume that the historical encryption key set is {0.2, 0.3, 0.4}, and the stage-adjusted encryption keys are 0.4, 0.7, and 0.9, and substitute them into the formula to calculate the loss: When a=0.4, Loss=− [(0.2∗ln0.4+0.8∗ln0.6)+(0.3∗ln0.4+0.7∗ln0.6)+(0.4∗ln0.4+0.6∗ln0.6)]≈0.63; when a=0.7, the loss is ≈0.95; when a=0.9, the loss is ≈1.64. Multiple stage-adjusted losses are obtained, with the maximum value being 1.64. The corresponding stage-adjusted encryption key 0.9 is extracted as the directional stage-adjusted key and the current optimal adjustment direction.

[0091] Next, the directional stage adjustment key (0.9) is updated to the adjustment direction, and the remaining stage adjustment encryption keys (0.4, 0.7) are adjusted twice according to the directional adjustment bandwidth (0.3). For example, with 0.9 as the center, 0.4 and 0.7 are adjusted in the range of [0.9-0.3,1] to obtain multiple follow-up adjustment encryption keys (such as 0.6, 0.8), where the key value in the key security loss function must be in the range of [0,1].

[0092] Finally, the key security loss function is used to calculate the loss amount of multiple follow-up adjusted encryption keys and the historical encryption key set. If there is a follow-up adjustment loss amount that is greater than or equal to the loss amount corresponding to the directional stage adjusted encryption key, the follow-up adjusted encryption key corresponding to the maximum value is updated to the directional stage adjusted encryption key, and then based on this, the remaining follow-up adjusted encryption keys are adjusted according to the directional adjustment bandwidth until the preset number of updates is met to obtain the adjusted encryption key. The specific steps are described in detail in A3242-5A-3242-5B.

[0093] By randomly adjusting the candidate keys, quantifying the loss to screen the optimal direction, and iteratively updating the adjustment direction, a directional optimization update of the encryption key is achieved, which maximizes the difference between the key and the historical set within a limited number of iterations and strengthens the security of signal baseline encryption.

[0094] Furthermore, step A342-5 in the method provided in the embodiment of the present application includes:

[0095] A3242-5A: Use the key security loss function to calculate the loss amount of the multiple follow-up adjusted encryption keys and the historical encryption key set to obtain multiple follow-up adjusted loss amounts.

[0096] A3242-5B: When there is a following adjustment loss amount among the multiple following adjustment loss amounts that is greater than or equal to the stage adjustment loss amount corresponding to the directional stage adjustment encryption key, the following adjustment encryption key corresponding to the maximum value of the multiple following adjustment loss amounts is updated to the directional stage adjustment encryption key, and the remaining multiple following adjustment encryption keys are again directionally adjusted according to the directional adjustment bandwidth based on the directional stage adjustment encryption key until the preset number of updates is met to obtain the adjustment encryption key.

[0097] In one embodiment, first, the key security loss function is used , multiple follow-up adjustment encryption keys and the historical encryption key set are substituted into the calculation one by one to obtain the follow-up adjustment loss corresponding to each follow-up adjustment encryption key. For example, assuming the historical encryption key set is {0.2, 0.3, 0.4}, the directional phase adjustment encryption key is 0.9 (corresponding to a loss of 1.64), and the follow-up adjustment encryption keys are 0.6 and 0.8, the calculation shows that when a=0.6, Loss=− [(0.2∗ln0.6+0.8∗ln0.4)+(0.3∗ln0.6+0.7∗ln0.4)+(0.4∗ln0.6+0.6∗ln0.4)]=0.79; when a=0.8, Loss=− [(0.2∗ln0.8+0.8∗ln0.2)+(0.3∗ln0.8+0.7∗ln0.2)+(0.4∗ln0.8+0.6∗ln0.2)]≈1.19.

[0098] The following adjustment loss is then compared with the phase-adjusted loss of the current directional phase-adjusted encryption key (1.64). If there is a following adjustment loss greater than or equal to this value (assuming the following adjustment key loss in the next round of adjustment is 2.11), the following adjustment key corresponding to the maximum value (e.g., the key 0.95 corresponding to 2.11) is updated as the new directional phase-adjusted encryption key. Next, based on the new direction, the remaining following adjustment keys are further adjusted according to the directional adjustment bandwidth (e.g., 0.95). For example, the original following adjustment keys 0.6 and 0.8 are adjusted to 0.2 and 0.6 (centered on the new direction 0.95, with a bandwidth of ±0.95, indicating that the following adjustment keys are adjusted within the interval [0, 1] with 0.95 as the reference value). The loss is repeatedly calculated and the direction is updated until a preset number of updates is reached. The preset number of updates should ensure that the loss approaches the threshold of the collaborative encryption asynchrony constraint during the iteration. If the loss is close to the threshold after the first iteration, a smaller number of iterations (such as 3-5 times) can be set; if the convergence is slow, the number of iterations needs to be increased until the loss reaches the standard, and then the adjusted encryption key is obtained.

[0099] Through a cyclic mechanism of loss calculation, direction evaluation and iterative adjustment, adaptive iterative update of encryption keys is achieved, which maximizes the difference between the key and the historical set within a limited number of times and significantly improves the signal baseline encryption security and anti-tampering capability.

[0100] Furthermore, step A5000 in the method provided in the embodiment of the present application includes:

[0101] A5100: Extracting a historical test log sequence from the window backtracking unit.

[0102] A5200: Extract associated words from the first historical test log in the historical test log sequence according to a preset abnormal associated word set, and add the extracted result to an empty vector to obtain a first memory.

[0103] A5300: Extract associated words from a second historical test log in the historical test log sequence based on the preset abnormal associated words and the first memory, and update the first memory according to the extraction result to obtain a second memory.

[0104] A5400: This process is deduced in this way until the end of the historical test log sequence is reached, and the time series iteration memory is obtained.

[0105] A5500: Utilize an exception information extractor to identify the time series iterative memory and obtain exception information.

[0106] In this embodiment, anomaly-related terms are a set of predefined keywords used to identify anomalies in production test logs. Sequential iterative memory is a temporal association memory vector constructed by iteratively processing a sequence of historical test logs. Anomaly information extractors are tools for analyzing and identifying sequential iterative memory.

[0107] Optionally, first, a historical test log sequence is extracted from the window backtracking unit of the cloud data platform, where the sequence contains production test process records (such as test time, test item results, signal reference values, etc.) arranged in chronological order.

[0108] Next, the first historical test log is decomposed at the word level to generate a word sequence set, and the set is traversed to perform semantic similarity matching with the preset abnormal related word set. The successfully matched related words are added to the empty vector to obtain the first memory. The specific steps are described in detail in A5210-A5220.

[0109] Then, based on the preset set of abnormality-related words and the first memory, the second historical test log is processed. In addition to matching the preset keywords, an extended match is performed with the associated words in the first memory (for example, "the voltage value in the first log exceeds the limit"). If the second log contains an abnormal current value, where the abnormality is a preset word and the current value has a semantic association with the voltage value in the first memory (both are physical parameters), the abnormal current value is added to the vector, and the first memory is updated to contain the second memory containing the new keywords. This process continues in this way. Each time a log is processed, the previous memory is iteratively updated until all logs are processed, forming a time-series iterative memory containing temporal association information.

[0110] Finally, when using the anomaly information extractor to analyze the time-series iterative memory, the historical test log sequence is first broken down line by line into a set of word sequences using a preset set of anomaly-related words. After semantic similarity matching, a memory vector containing the time-series associations is generated (such as the first memory, the second memory, and so on, until a complete time-series iterative memory is formed). Next, an association rule mining algorithm (such as the Apriori algorithm) is used to analyze the frequency and temporal relationships of the keywords in the memory vectors. Support thresholds (e.g., at least 20% of the logs contain a certain keyword combination) and confidence thresholds (e.g., the probability of triggering an anomaly when a certain combination appears is ≥ 80%) are set to identify anomaly patterns with high-frequency associations. For example, when voltage out-of-limit and current anomalies appear continuously in the time-series memory and the frequency of associated test failures exceeds a preset threshold, the association rule engine triggers an anomaly determination. Combined with the production test business logic, this is mapped to the power module anomaly type, ultimately outputting specific anomaly information and associated factors.

[0111] By constructing time-series iterative memories and conducting semantic association analysis on historical test logs, we have achieved the goal of accurately extracting abnormal information from massive logs, thereby improving the efficiency of locating abnormalities in the production and testing process and strengthening data security monitoring capabilities.

[0112] Furthermore, step A5200 in the method provided in the embodiment of the present application includes:

[0113] A5210: Decompose the first historical test log at the word level to generate a word sequence set.

[0114] A5220: Traverse the word sequence set and perform semantic similarity matching with the preset abnormal associated word set. If the match is successful, add it as an associated word into the empty vector to obtain the first memory.

[0115] In one embodiment, the first historical test log is first decomposed at the word level, dividing the log text into independent word units to generate a word sequence set. For example, if the first historical test log is about the power module voltage exceeding the limit and the test failing, the decomposition results in a word sequence set of [power, module, voltage, exceeding the limit, test, failure].

[0116] Next, each word in the word sequence set is traversed and semantically matched against a preset set of abnormality-related words. The preset abnormality-related word set contains common industry abnormality description terms, such as [overrun, abnormality, failure, error, interruption]. The cosine similarity algorithm is used to calculate the semantic similarity between words. A matching threshold is set (e.g., similarity ≥ 0.7). If a word in the word sequence successfully matches a related word in the preset set, it is added to the empty vector. For example, if the words "overrun" and "failure" in the word sequence exactly match the words "overrun" and "failure" in the preset set, the voltage value is filtered out if there is no direct match in the preset set, and the first memory vector containing [overrun, failure] is ultimately generated.

[0117] Furthermore, when calculating semantic similarity between words using the cosine similarity algorithm, the first historical test log is first broken down at the word level to generate a set of word sequences (for example, "voltage value exceeds limit" is broken down into "voltage value" and "exceed limit"). Each word is then converted into a vector representation (for example, using Word2Vec to generate word vectors). The cosine value of the angle between the word vectors is then calculated. If the value is ≥ 0.7 (for example, the cosine value of "exceed limit" and the preset word "abnormal" is 0.8), the match is considered successful. The successfully matched word is added to the empty vector to form the first memory.

[0118] By performing word-level decomposition, semantic similarity matching, and association vector construction on historical test logs, we achieved the goal of efficiently extracting anomaly-related features from unstructured log data, providing structured input for time-series iterative memory analysis and improving the efficiency of locating production test anomalies.

[0119] In summary, the FCT secure collaborative scheduling method based on full-link data fusion provided by the embodiments of the present application has the following technical effects:

[0120] This application parses the production test plan through the cloud data platform to obtain the production test item sequence and instruction sequence, sends it to the FCT client to match the responsible chain execution engine, tests the production test items based on the execution engine sequence and production test instructions, verifies the signal benchmark security through the full-link staggered collaborative encryption verification strategy, and generates test verification results. If the test passes, the log is partitioned and stored and the next production test item is executed. If it fails, the window backtracking unit data is extracted for time series iterative memory analysis to determine the abnormal information to coordinate the scheduling of the execution engine. At the same time, the signal benchmark is extracted to use the log analysis timestamp minimum value to trigger the encryption update instruction, and the initial key is generated in combination with the historical encryption key set. The loss amount is calculated through the key security loss function, the encryption key is adjusted and encrypted to achieve signal benchmark security verification, thereby accurately performing production test scheduling and data security protection, making the FCT production test process security collaboration and data scheduling more accurate and reliable, and achieving the technical effect of using the security collaboration mechanism to realize the differentiated management of factory data and tampering prevention of signal benchmarks, combining the full-link data scheduling to optimize the test process, and improving data security, management isolation and enterprise compliance.

[0121] Example 2, as Figure 2 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides an FCT secure collaborative scheduling system based on full-link data fusion, the system comprising:

[0122] The production test sequence acquisition module 1 is used to use the cloud data platform to analyze the production test plan according to the target test product and test requirements, and obtain the production test item sequence and the production test instruction sequence.

[0123] The engine sequence acquisition module 2 is used to send the production test item sequence and the production test instruction sequence to the FCT client, use the FCT client to perform responsible chain execution engine matching, and obtain the execution engine sequence.

[0124] A test result generation module 3 tests the production test item sequence in accordance with the production test instruction sequence based on the execution engine sequence. During the test, the corresponding signal benchmark is security verified through the full-link staggered collaborative encryption verification strategy. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result.

[0125] The test execution module 4 is used to upload the test log to the cloud data platform for partitioned storage when the test verification result shows that the production test item has passed, and execute the test of the next production test item in the order of the production test item sequence from front to back until all production test items are tested.

[0126] Collaborative scheduling execution module 5, the collaborative scheduling execution module 5 is used to upload the test log to the cloud data platform for partitioned storage when the test verification result is that the production test item fails, and simultaneously extract the data stored in the window backtracking unit in the cloud data platform for time series iterative memory analysis, determine the abnormal information, and perform safe collaborative scheduling on the corresponding execution engine based on the abnormal information.

[0127] Furthermore, the test result generating module 3 is used to perform the following steps:

[0128] Extract the signal benchmark staggered encryption update constraint and the collaborative encryption asynchronous constraint in the full-link staggered collaborative encryption verification strategy; obtain the usage log sequence set of the full-link signal benchmark of the production test item sequence, analyze the minimum value from the timestamp of the usage log to the current time window in the usage log sequence set, and judge whether the minimum value meets the signal benchmark staggered encryption update constraint; if so, perform security verification based on the encryption key of the signal benchmark; if not, trigger the signal benchmark staggered encryption update instruction, and according to the signal benchmark staggered encryption update instruction, combine the usage log sequence set and the collaborative encryption asynchronous constraint to update the encryption key of the signal benchmark, and perform security verification of the signal benchmark based on the updated updated encryption key.

[0129] Furthermore, the test result generating module 3 is used to perform the following steps:

[0130] Extract the encryption keys from the usage log sequence set to obtain a historical encryption key set; use a random number generator to generate an initial encryption key; use a key security loss function to perform loss analysis on the initial encryption key and the historical encryption key set to determine the initial loss amount; determine whether the initial loss amount meets the collaborative encryption asynchronous constraint; if not, perform a direction adjustment on the initial encryption key to obtain an adjusted encryption key; when the adjusted encryption key meets the collaborative encryption asynchronous constraint, use a key encryption key to encrypt the adjusted encryption key to obtain an updated updated encryption key.

[0131] Furthermore, the test result generating module 3 is used to perform the following steps:

[0132] The key security loss function is: ;in, is the initial loss amount, is the initial encryption key, n is the total number of historical encryption keys in the historical encryption key set and is a positive integer, is the i-th historical encryption key in the historical encryption key set.

[0133] Furthermore, the test result generating module 3 is used to perform the following steps:

[0134] Determine whether the initial loss amount is greater than or equal to the loss amount threshold in the collaborative encryption asynchronous constraint; if not, determine a directional adjustment bandwidth based on the size of the initial loss amount; and randomly adjust the initial encryption key based on the directional adjustment bandwidth, with the adjustment direction being away from the historical encryption key set, to obtain the adjusted encryption key.

[0135] Furthermore, the test result generating module 3 is used to perform the following steps:

[0136] The initial encryption key is randomly adjusted according to the directional adjustment bandwidth to obtain multiple stage-adjusted encryption keys; the key security loss function is used to traverse and calculate the loss amounts of the multiple stage-adjusted encryption keys and the historical encryption key set to obtain multiple stage-adjusted loss amounts; the stage-adjusted encryption key corresponding to the maximum value is extracted from the multiple stage-adjusted loss amounts, and is used as the directional stage-adjusted encryption key; the directional stage-adjusted encryption key is updated to the adjustment direction, and the remaining multiple stage-adjusted encryption keys are adjusted according to the directional adjustment bandwidth to obtain multiple follow-up adjustment encryption keys; the directional stage-adjusted encryption key is updated based on the multiple follow-up adjustment encryption keys until a preset number of updates is met to obtain the adjusted encryption key.

[0137] Furthermore, the test result generating module 3 is used to perform the following steps:

[0138] A key security loss function is used to calculate the loss amount of the multiple follow-up adjustment encryption keys and the historical encryption key set to obtain multiple follow-up adjustment loss amounts; when there is a follow-up adjustment loss amount among the multiple follow-up adjustment loss amounts that is greater than or equal to the stage adjustment loss amount corresponding to the directional stage adjustment encryption key, the follow-up adjustment encryption key corresponding to the maximum value of the multiple follow-up adjustment loss amounts is updated to the directional stage adjustment encryption key, and the remaining multiple follow-up adjustment encryption keys are directionally adjusted according to the directional adjustment bandwidth based on the directional stage adjustment encryption key until the preset number of updates is met to obtain the adjusted encryption key.

[0139] Furthermore, the collaborative scheduling execution module 5 is configured to perform the following steps:

[0140] A historical test log sequence is extracted from the window backtracking unit; associated words are extracted from the first historical test log in the historical test log sequence according to a preset abnormal associated word set, and the extraction result is added to an empty vector to obtain a first memory; associated words are extracted from the second historical test log in the historical test log sequence based on the preset abnormal associated words and the first memory, and the first memory is updated according to the extraction result to obtain a second memory; and so on, until the end of the historical test log sequence is reached, and a time series iterative memory is obtained; the time series iterative memory is identified using an abnormal information extractor to obtain abnormal information.

[0141] Furthermore, the collaborative scheduling execution module 5 is configured to perform the following steps:

[0142] The first historical test log is disassembled at the word level to generate a word sequence set; the word sequence set is traversed and semantic similarity matching is performed with the preset abnormal related word set. If the match is successful, it is added as a related word into the empty vector to obtain the first memory.

[0143] The FCT safe collaborative scheduling system based on full-link data fusion provided in an embodiment of the present invention can execute the FCT safe collaborative scheduling method based on full-link data fusion provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0144] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0145] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The FCT secure collaborative scheduling method based on full-link data fusion is characterized by: The method comprises: Use the cloud data platform to analyze the production test plan based on the target test products and test requirements, and obtain the production test item sequence and production test instruction sequence; Sending the production test item sequence and the production test instruction sequence to the FCT client, and using the FCT client to perform responsible chain execution engine matching to obtain an execution engine sequence; Based on the execution engine sequence, the production test item sequence is tested in sequence according to the production test instruction sequence. During the test, the corresponding signal benchmark is security verified using the full-link staggered collaborative encryption verification strategy. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result. When the test verification result shows that the production test item has passed, the test log is uploaded to the cloud data platform for partitioned storage, and the test of the next production test item is executed in the order of the production test item sequence from the front to the back until all production test items are tested. When the test verification result shows that the production test item fails, the test log is uploaded to the cloud data platform for partitioned storage, and the data stored in the window backtracking unit in the cloud data platform is simultaneously extracted for time series iterative memory analysis to determine the abnormal information. Based on the abnormal information, the corresponding execution engine is safely coordinated and scheduled; When the production test item sequence is tested in sequence according to the production test instruction sequence based on the execution engine sequence, the corresponding signal benchmark is security verified through the full-link staggered collaborative encryption verification strategy. If the verification is successful, test verification is performed based on the verified signal benchmark to generate a test verification result, including: Extracting the signal reference staggered encryption update constraint and the collaborative encryption asynchronous constraint in the full-link staggered collaborative encryption verification strategy; Obtain a usage log sequence set of the full-link signal benchmark of the production test item sequence, analyze the minimum value from the timestamp of the usage log in the usage log sequence set to the current time window, and determine whether the minimum value meets the signal benchmark staggered encryption update constraint; if so, perform security verification based on the encryption key of the signal benchmark; if not, trigger a signal benchmark staggered encryption update instruction, and according to the signal benchmark staggered encryption update instruction, perform an encryption key update on the signal benchmark in combination with the usage log sequence set and the collaborative encryption asynchronous constraint, and perform security verification on the signal benchmark based on the updated updated encryption key; The method includes updating the encryption key of the signal reference according to the signal reference staggered encryption update instruction, combining the use of a log sequence set and the collaborative encryption asynchronous constraint, and performing security verification of the signal reference based on the updated encryption key, including: Extracting encryption keys from the usage log sequence set to obtain a historical encryption key set; Generate an initial encryption key using a random number generator; Performing a loss analysis on the initial encryption key and the historical encryption key set using a key security loss function to determine an initial loss amount; Determining whether the initial loss amount satisfies the collaborative encryption asynchronous constraint; if not, performing a direction adjustment on the initial encryption key to obtain an adjusted encryption key; When the adjusted encryption key satisfies the collaborative encryption asynchronous constraint, encrypting the adjusted encryption key using the key encryption key to obtain an updated updated encryption key; Among them, the key security loss function is: ； in, is the initial loss amount, is the initial encryption key, n is the total number of historical encryption keys in the historical encryption key set and is a positive integer, is the i-th historical encryption key in the historical encryption key set.

2. The FCT secure collaborative scheduling method based on full-link data fusion according to claim 1 is characterized in that: Determining whether the initial loss amount satisfies the collaborative encryption asynchronous constraint, and if not, performing a direction adjustment on the initial encryption key to obtain an adjusted encryption key, including: Determining whether the initial loss amount is greater than or equal to a loss amount threshold in the collaborative encryption asynchronous constraint, and if not, determining a directional adjustment bandwidth based on the size of the initial loss amount; The initial encryption key is randomly adjusted based on the directional adjustment bandwidth, with the adjustment direction being away from the historical encryption key set, to obtain the adjusted encryption key.

3. The FCT secure collaborative scheduling method based on full-link data fusion according to claim 2 is characterized in that: The method includes randomly adjusting the initial encryption key based on the directional adjustment bandwidth in a direction away from the historical encryption key set to obtain the adjusted encryption key, including: Randomly adjusting the initial encryption key according to the directional adjustment bandwidth to obtain multiple stages of adjusted encryption keys; Using the key security loss function, traversing and calculating the loss amounts of the multiple-stage adjusted encryption keys and the historical encryption key set to obtain multiple-stage adjusted loss amounts; extracting a phase-adjusted encryption key corresponding to a maximum value from the plurality of phase-adjusted loss amounts, and using the maximum value as a directional phase-adjusted encryption key; Updating the directional stage adjustment encryption key to the adjustment direction, and adjusting the remaining multiple stage adjustment encryption keys according to the directional adjustment bandwidth to obtain multiple follow-up adjustment encryption keys; The directional phase adjusted encryption key is updated based on the multiple follow-up adjusted encryption keys until a preset number of updates is met to obtain the adjusted encryption key.

4. The FCT secure collaborative scheduling method based on full-link data fusion according to claim 3 is characterized in that: The method of updating the directional phase adjusted encryption key based on the multiple follow-up adjusted encryption keys until a preset number of updates is met to obtain the adjusted encryption key includes: Calculating loss amounts of the multiple follow-up adjusted encryption keys and the historical encryption key set using a key security loss function to obtain multiple follow-up adjusted loss amounts; When there is a following adjustment loss amount among the multiple following adjustment loss amounts that is greater than or equal to the stage adjustment loss amount corresponding to the directional stage adjustment encryption key, the following adjustment encryption key corresponding to the maximum value of the multiple following adjustment loss amounts is updated to the directional stage adjustment encryption key, and the remaining multiple following adjustment encryption keys are again directionally adjusted according to the directional adjustment bandwidth based on the directional stage adjustment encryption key until the preset number of updates is met to obtain the adjustment encryption key.

5. The FCT secure collaborative scheduling method based on full-link data fusion according to claim 1 is characterized in that: include: Extracting a historical test log sequence from the window backtracking unit; Extracting associated words from the first historical test log in the historical test log sequence according to a preset abnormal associated word set, and adding the extracted result to an empty vector to obtain a first memory; Extracting associated words from a second historical test log in the historical test log sequence based on the preset abnormal associated words and the first memory, and updating the first memory according to the extraction result to obtain a second memory; This process is deduced in this way until the end of the historical test log sequence is reached, and the time series iteration memory is obtained; An abnormal information extractor is used to identify the time series iterative memory to obtain abnormal information.

6. The FCT secure collaborative scheduling method based on full-link data fusion according to claim 5 is characterized in that: Extracting associated words from the first historical test log in the historical test log sequence according to a preset abnormal associated word set, and adding the extracted result to an empty vector to obtain a first memory, including: Decomposing the first historical test log at a word level to generate a word sequence set; The word sequence set is traversed and semantic similarity matching is performed with the preset abnormal associated word set. If the match is successful, it is added as an associated word into the empty vector to obtain the first memory.

7. The FCT safety collaborative scheduling system based on full-link data fusion is characterized by: For implementing the FCT secure collaborative scheduling method based on full-link data fusion according to any one of claims 1 to 6, the system comprises: The production test sequence acquisition module is used to use the cloud data platform to analyze the production test plan based on the target test product and test requirements, and obtain the production test item sequence and production test instruction sequence; An engine sequence acquisition module is used to send the production test item sequence and the production test instruction sequence to the FCT client, and use the FCT client to perform responsible chain execution engine matching to obtain an execution engine sequence; A test result generation module tests the production test item sequence according to the production test instruction sequence based on the execution engine sequence, and performs security verification on the corresponding signal benchmark through the full-link staggered collaborative encryption verification strategy during the test. If the verification is successful, the signal benchmark is used for test verification to generate a test verification result; The test execution module is used to upload the test log to the cloud data platform for partition storage when the test verification result shows that the production test item has passed, and execute the test of the next production test item in the order of the production test item sequence from the front to the back until all production test items are tested; The collaborative scheduling execution module is used to upload the test log to the cloud data platform for partitioned storage when the test verification result is that the production test item fails, and simultaneously perform time-series iterative memory analysis on the data stored in the window backtracking unit in the cloud data platform to determine the abnormal information, and perform safe collaborative scheduling on the corresponding execution engine based on the abnormal information.

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