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

Abstract

The invention discloses an FCT security collaborative scheduling method and system based on full-link data fusion, and relates to the technical field of data processing and cloud computing, and the method comprises the steps: a cloud data platform analyzes items and instruction sequences of a yield measurement plan; issuing to the FCT client to match an execution engine sequence; testing the production test items in sequence, verifying the signal reference safety through encryption verification, and generating a result; if yes, storing the log and executing the next item; if not, storing the log, analyzing window backtracking data to determine abnormity, and scheduling an execution engine. The technical problems that in factory data scheduling, management isolation is insufficient, a signal reference lacks a safety cooperation protection mechanism in the production and measurement process, tampering is prone to occurring, data are mixed, and safety is not up to the standard are solved, and the purposes of achieving differentiated management of factory data and tampering prevention and control of the signal reference through the safety cooperation mechanism and improving the safety of factory data are achieved. And a full-link data scheduling optimization test process is combined, so that the technical effects of data security, management isolation and enterprise compliance are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of data processing and cloud computing, and particularly to an FCT security collaborative scheduling method and system based on full-link data fusion. Background Art

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

[0003] The present application provides an FCT security 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, being easily tampered with, data mixing, and insufficient security.

[0004] In the first aspect of the present application, a FCT security collaborative scheduling method based on full-link data fusion is provided. The method includes: parsing a production test plan by using a cloud data platform according to a target test product and test requirements to 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 an FCT client, and using the FCT client to perform a responsible chain execution engine matching to obtain an execution engine sequence; based on the execution engine sequence, sequentially testing the production test item sequence according to the production test instruction sequence, and performing security verification on a corresponding signal reference through a full-link time-offset collaborative encryption verification strategy during the test. If the verification is successful, then using the signal reference to perform a test verification to generate a test verification result; when the test verification result indicates that a production test item passes, uploading the test log to the cloud data platform for partition storage, and sequentially performing the test of the next production test item in the order from front to back of the production test item sequence until all production test items are tested; when the test verification result indicates that a production test item fails, uploading the test log to the cloud data platform for partition storage, and simultaneously performing a time-series iterative memory analysis on the data stored in the window backtracking unit in the cloud data platform to determine abnormal information, and performing a security collaborative scheduling on the corresponding execution engine based on the abnormal information; wherein, when sequentially testing the production test item sequence according to the production test instruction sequence based on the execution engine sequence, performing security verification on a corresponding signal reference through a full-link time-offset collaborative encryption verification strategy. If the verification is successful, then performing a test verification based on the verified signal reference to generate a test verification result, including: extracting a signal reference time-offset encryption update constraint and a collaborative encryption asynchronous constraint in the full-link time-offset collaborative encryption verification strategy; obtaining a usage log sequence set of the full-link signal reference 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 determining whether the minimum value satisfies the signal reference time-offset encryption update constraint; if so, performing security verification based on the encryption key of the signal reference; if not, triggering a signal reference time-offset encryption update instruction, and updating the encryption key of the signal reference according to the signal reference time-offset encryption update instruction, in combination with the usage log sequence set and the collaborative encryption asynchronous constraint, and performing security verification of the signal reference based on the updated encryption key.

[0005] Preferably, according to the signal reference time-shifted encryption update instruction, the encryption key of the signal reference is updated by combining the use log sequence set and the collaborative encryption asynchronous constraint, and the security verification of the signal reference is performed based on the updated encryption key, including: extracting the encryption key in the use log sequence set to obtain the historical encryption key set; generating an initial encryption key using a random number generator; performing loss analysis on the initial encryption key and the historical encryption key set using a key security loss function to determine the initial loss amount; determining whether the initial loss amount satisfies the collaborative encryption asynchronous constraint, if not, performing directional 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 a key encryption key to obtain the updated encryption key.

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

[0007] Preferably, determining whether the initial loss amount satisfies the collaborative encryption asynchronous constraint, if not, performing directional 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 the loss amount threshold in the collaborative encryption asynchronous constraint, if not, determining the directional adjustment bandwidth based on the magnitude of the initial loss amount; taking the direction away from the historical encryption key set as the adjustment direction, and randomly adjusting the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key.

[0008] Preferably, taking the direction away from the set of historical encryption keys as the adjustment direction, randomly adjusting the initial encryption key 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 to iteratively calculate the loss amounts between the multiple stage-adjusted encryption keys and the set of historical encryption keys 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 as the directional stage-adjusted encryption key; updating the directional stage-adjusted encryption key as the adjustment direction, and adjusting the remaining multiple stage-adjusted encryption keys according to the directional adjustment bandwidth to obtain multiple following-adjusted encryption keys; updating the directional stage-adjusted encryption key based on the multiple following-adjusted encryption keys until a preset number of updates is satisfied to obtain the adjusted encryption key.

[0009] Preferably, the method includes: updating the directional stage-adjusted encryption key based on the multiple following-adjusted encryption keys until a preset number of updates is satisfied to obtain the adjusted encryption key, including: using the key security loss function to calculate the loss amounts between the multiple following-adjusted encryption keys and the set of historical encryption keys to obtain multiple following-adjusted loss amounts; when there is a following-adjusted loss amount greater than or equal to the stage-adjusted loss amount corresponding to the directional stage-adjusted encryption key among the multiple following-adjusted loss amounts, updating the following-adjusted encryption key corresponding to the maximum value among the multiple following-adjusted loss amounts as the directional stage-adjusted encryption key, and again performing directional adjustment on the remaining multiple following-adjusted encryption keys according to the directional adjustment bandwidth based on the directional stage-adjusted encryption key until a preset number of updates is satisfied to obtain the adjusted encryption key.

[0010] Preferably, extracting a historical test log sequence from the window backtracking unit; extracting correlation words from the first historical test log in the historical test log sequence according to a preset set of abnormal correlation words and adding the extraction result into an empty vector to obtain a first memory; extracting correlation words from the second historical test log in the historical test log sequence based on the preset abnormal correlation words and the first memory, and updating the first memory according to the extraction result to obtain a second memory; and so on until the end of the historical test log sequence is reached to obtain a time-sequential iterative memory; using an abnormal information extractor to identify the time-sequential iterative memory to obtain abnormal information.

[0011] 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 to perform semantic similarity matching with the preset abnormal associated word set, and if the match is successful, adding it as an associated word to the empty vector to obtain the first memory.

[0012] 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, which is used to use the cloud data platform to perform production test plan analysis according to the target test product and test requirements, and obtain the production test item sequence and the production test instruction sequence; an engine sequence acquisition module, which 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 the execution engine sequence; a test result generation module, which tests 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 the full-link staggered collaborative encryption verification 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 not passed, 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 security collaborative scheduling on the corresponding execution engine based on the abnormal information.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application analyzes 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. After being matched by the responsible chained execution engine, the execution engine sequence is obtained. When performing tests based on this sequence, the security of the signal reference is verified through the full-link asynchronous collaborative encryption verification strategy. According to the test verification results, the logs are stored in partitions or the time-series iterative memory analysis is performed to determine the abnormal information and schedule the execution engine, so as to realize the secure collaborative scheduling of the FCT test process and the full-link integration management of data, reduce the risk of signal reference tampering during the production test process, significantly improve the data isolation and the accuracy of the test results, and achieve the technical effects of using the secure collaborative mechanism to realize the differentiated management of factory data and the prevention and control of signal reference tampering, combining the full-link data scheduling to optimize the test process, and improving the data security, management isolation and enterprise compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a flowchart of the FCT secure collaborative scheduling method based on full-link data fusion provided by the embodiment of this application.

[0016] Figure 2 It is a structural diagram of the FCT secure collaborative scheduling system based on full-link data fusion provided by the embodiment of this application.

[0017] Description of the reference numerals: The production test sequence acquisition module 1, the engine sequence acquisition module 2, the test result generation module 3, the test execution module 4, the collaborative scheduling execution module 5. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] 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 references during the production test process, and being prone to tampering, data mixing and non-compliance with security standards.

[0019] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in this application belong to the scope of protection of this application.

[0020] It should be noted that the terms "first", "second", etc. in the specification of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0021] Embodiment 1, as Figure 1 shown, is an FCT security collaborative scheduling method based on full-link data fusion. Among them, the method includes: Step A1000: Use the cloud data platform to parse 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.

[0022] In the embodiments of this application, the production test plan is an overall test plan formulated based on the target test 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, which is used to clarify each test item to be executed during the production test and its order. The production test instruction sequence is a set of instructions corresponding one by one to the production test item sequence, which is used to guide the specific execution of each production test item.

[0023] Specifically, first, those skilled in the art input the model, specifications and specific test requirements of the target test product (such as functional test, performance stability test, etc.). The cloud data platform performs structured processing on the input information through a built-in parsing engine. The parsing engine decomposes the abstract test requirements into an executable production test item sequence based on a preset industry standard template or enterprise custom rules. For example, if the target test product is a certain model power adapter and the test requirement is to verify its output performance, then after parsing, production test items such as input voltage stability test, output current overload protection test, and ripple coefficient detection can be generated, and each production test item is arranged in a sequence according to the test logic order.

[0024] After obtaining the production test item sequence, the cloud data platform retrieves the corresponding production test instructions from the underlying database according to the mapping relationship between the production test items and the production test instructions. The production test instructions include test limit values (such as the allowable voltage fluctuation range of the input voltage 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, in its corresponding production test instructions, when the test limit value is set such that the grid voltage is within the range of 190V - 240V, the output voltage fluctuation of the adapter needs to be ≤ ±0.5V; the signal reference data is the rated input voltage of 220VAC and the rated output voltage of 12VDC; the execution module is a dedicated voltage acquisition module integrated in the FCT client. The platform ensures the one-to-one correspondence between each production test item and the production test instruction through a data verification mechanism, avoiding the occurrence of missing or mismatched instructions.

[0025] Through the automated parsing engine, standardized data mapping mechanism, and centralized database management of the cloud data platform, the accurate conversion from test requirements to executable production test sequences is achieved, resulting in the effects of improving the efficiency of formulating production test plans, reducing human errors, and enhancing the maintainability of test standards.

[0026] Step A2000: Send the production test item sequence and the production test instruction sequence to the FCT client, and use the FCT client to perform a responsible chain execution engine matching to obtain an execution engine sequence.

[0027] In the embodiment of the present application, the FCT client is a terminal carrier for receiving the production test item sequence and the production test instruction sequence sent by the cloud data platform and performing responsible chain execution engine matching. The execution engine sequence is an ordered module list generated through responsible chain execution engine matching for guiding test execution.

[0028] Optionally, after sending the production test item sequence and the production test instruction sequence 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 the HTTP / HTTPS protocol) and verifies the data integrity (such as ensuring that the data has not been tampered with by comparing the MD5 hash value). After the verification passes, the client starts the responsible chain execution engine matching mechanism: generates a unique responsibility identifier for each production test item (for example, a combined identifier containing the production test item number, timestamp, and device ID), and binds the responsibility identifier to the execution module according to the correspondence between the execution module in the production test instruction sequence (such as the voltage detection module numbered FCT-032) and the production test item.

[0029] Subsequently, the execution engine matching module connects the responsibility identifiers and the execution modules in series in the logical order of the production test item sequence (for example, first perform input voltage test and then output current test) to form an execution engine sequence. For example, if the production test item sequence is input voltage stability test → output current overload protection test → ripple coefficient detection, the execution engine sequence will sequentially call the voltage detection module, the current protection module, and the ripple analysis module, and assign corresponding responsibility identifiers to each module to ensure that the next module is automatically triggered after the previous module finishes execution.

[0030] Through the responsibility-based chained execution engine matching mechanism of the FCT client, the abstract production test item sequence and instruction sequence are transformed into an automatically executable engine sequence, achieving the effects of improving the automation degree of the test process, ensuring the accuracy of the task execution order, and reducing the cost of human intervention.

[0031] Step A3000: Based on the execution engine sequence, sequentially test the production test item sequence according to the production test instruction sequence. During the test, perform security verification on the corresponding signal reference through the full-link time-offset collaborative encryption verification strategy. If the verification is successful, use the signal reference for test verification to generate a test verification result.

[0032] In the embodiment of the present application, the full-link time-offset collaborative encryption verification strategy is a strategy for performing security verification on the signal reference during the test. 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 expectation during the production test.

[0033] In an embodiment of the present application, first, the FCT client triggers the corresponding execution modules (such as the voltage detection module, the current protection module, etc.) in the order of the execution engine sequence. After each execution module is started, it retrieves the test limit values (such as the allowable voltage fluctuation range of the input voltage) and signal reference data (such as the standard output voltage value) that match the current production test item from the production test instruction sequence, and performs real-time data acquisition on the device under test based on this instruction. For example, during the input voltage stability test, the corresponding execution module will continuously collect the input voltage value of the device and dynamically compare it with the signal reference and the test limit values.

[0034] Then, during the test, extract the relevant constraints in the full-link time-offset collaborative encryption verification strategy, analyze the signal reference usage log timestamp. If the constraints are not met, trigger the encryption instruction, and update the encryption key in combination with the constraints to verify the security of the signal reference. The specific steps are described in detail in A3100 - A3200.

[0035] If the security verification of the signal reference is successful through the full-link staggered time collaborative encryption verification strategy, the FCT client 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 of the device under test according to the production test instructions (such as the input voltage stability test item corresponding to the voltage detection module), and dynamically compares it with the signal reference (such as the rated voltage of 220VAC) and the test limits (such as the fluctuation ≤ ±0.5V). If the data is within the limit range, it is determined that the production test item passes; otherwise, it is determined that it fails. The generated test verification results include information such as the signal reference version number, test time, and data comparison results, and are uploaded to the cloud data platform for partition storage to provide a complete data chain for subsequent full-process traceability or anomaly analysis.

[0036] Through the automated drive of the execution engine sequence, the precise matching of production test instructions, and the full-link traceability mechanism of test results, the effects of improving production test efficiency, reducing human errors, and ensuring data authenticity and traceability are achieved.

[0037] Step A4000: When the test verification result is that the production test item passes, upload the test log to the cloud data platform for partition storage, and execute the test of the next production test item in the order from front to back of the production test item sequence until all production test items are tested.

[0038] Specifically, when the test verification result is that the production test item passes, the FCT client first uploads the test log (including original test data, execution log, and failure item information) to the cloud data platform through a secure transmission protocol (such as HTTPS). The cloud data platform automatically creates a storage partition according to the preset partition rules (such as factory ID + production test date + device type), classifies and stores the logs according to the partition rules, and ensures physical isolation of data from different factories and batches. After the upload is completed, the execution engine automatically calls the next item in the production test item sequence, and repeats the aforementioned instruction matching → test execution → result verification process until all production test items are executed.

[0039] Through the partition storage mechanism of test logs and the automated sequential scheduling logic of the execution engine, the effects of improving data traceability, ensuring enterprise data security and compliance, and optimizing production test efficiency are achieved.

[0040] Step A5000: When the test verification result is that the production test item fails, upload the test log to the cloud data platform for partition storage, and simultaneously perform sequential iterative memory analysis on the data stored in the window backtracking unit in the cloud data platform to determine the abnormal information, and perform secure collaborative scheduling on the corresponding execution engine based on the abnormal information.

[0041] In the 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.

[0042] Specifically, when the test verification result is 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, which is stored in partitions according to the rules of factory ID + production test batch + device type.

[0043] At the same time, the system automatically triggers the window backtracking unit of the cloud data platform to extract the historical test log sequence, extracts the correlation words from the logs in the sequence in turn according to the preset set of abnormal correlation words, and iteratively updates the memory vector layer by layer until the end of the sequence to obtain the time-series iterative memory. The abnormal information extractor is used to identify and obtain the abnormal information, and the specific steps are described in detail in A5100 - A5500.

[0044] Next, based on the abnormal information, a full-link misaligned encryption verification is performed on the signal reference used for the abnormal test item. If the verification fails, a signal reference replacement request is automatically triggered; then the rescheduling method is judged. If the abnormality is caused by transient signal interference or cache error, etc., the current module can be partially re-executed, and at the same time, the test limit values (such as parameters like extending the response waiting time) can be modified according to the actual situation, or module replacement scheduling can be performed (such as replacing the abnormal main module A with the standby module B), or the execution order of the modules in the responsibility chain can be adjusted through path optimization scheduling (such as performing communication stability tests first and then response tests) 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, notifying the tester to review and select modules to reload or pause execution. Through the above methods, the safe collaborative scheduling of the execution engine is achieved, ensuring the accuracy and stability of the test process.

[0045] Through the partition 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, the rapid positioning of production test abnormalities and targeted responses are realized, achieving the effects of shortening the troubleshooting time, improving the security of the test process, and ensuring the reliability of test data.

[0046] Furthermore, step A3000 in the method provided in the embodiment of the present application includes: A3100: Extract the signal reference misaligned encryption update constraint and the collaborative encryption asynchronous constraint in the full-link misaligned collaborative encryption verification policy.

[0047] A3200: Obtain the set of usage log sequences of the full-link signal benchmark for the production test item sequence, analyze the minimum value from the timestamp of the usage logs in the set of usage log sequences to the current time window, and determine whether the minimum value meets the time-offset encryption update constraint for the signal benchmark; if so, perform security verification based on the encryption key of the signal benchmark; if not, trigger the time-offset encryption update instruction for the signal benchmark, and according to the time-offset encryption update instruction for the signal benchmark, combine the set of usage log sequences 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 encryption key.

[0048] Specifically, when testing the production test item sequence according to the production test instruction sequence based on the execution engine sequence in turn, first, accurately extract two types of key constraints from the full-link time-offset collaborative encryption verification policy: one is the time-offset encryption update constraint for the signal benchmark, for example, clearly set the time interval threshold for encryption update (such as 30 minutes), and force the encryption update operation to be performed once every such time period; the other is the collaborative encryption asynchronous constraint, that is, the loss amount threshold constraint in the key security loss function. For example, the loss amount between the newly generated encryption key and the set of historical encryption keys needs to be greater than or equal to this loss amount threshold to ensure that the difference between the new key and the historical keys is large enough to avoid problems caused by the synchronization of encryption operations. The combination of the two realizes two-factor dynamic authentication of execution time + execution path, ensuring the security and dynamic adaptability of signal benchmark encryption verification.

[0049] Then, obtain the set of full-link signal benchmark usage log sequences for the production test item sequence. This set completely records detailed information such as the timestamp of each signal benchmark usage, the associated module ID, the historical encryption time, and the status (such as specific log entries including 2024-04-20 10:00:00, module A, last encryption time 2024-04-20 09:30:00, etc.), providing comprehensive data support for subsequent analysis of the usage characteristics and encryption status of the signal benchmark. Analyze the timestamp of each usage log in the set, calculate the minimum value from the timestamp to the current time window (for example, the current time window is 1 hour, a certain usage log timestamp is 50 minutes from the current time, and another log is 400 minutes from the current time, then the minimum value is 40 minutes), and determine whether this minimum value meets the time-offset encryption update constraint for the signal benchmark. If it meets, there is no need to update the encryption key. At this time, perform security verification based on the encryption key of the signal benchmark. If it does not meet (such as the constraint requires updating every 30 minutes, and the minimum value is 40 minutes), then trigger the time-offset encryption update instruction for the signal benchmark.

[0050] Then, extract the encryption keys in the usage log sequence set to form a historical encryption key set, generate an initial encryption key using a random number generator, analyze its initial loss amount with the historical set through a key security loss function, and adjust the initial encryption key if the collaborative encryption asynchronous constraint is not satisfied. After satisfying the constraint, encrypt the adjusted encryption key using a key encryption key to obtain an updated encryption key to verify the signal benchmark security. The specific steps are described in detail in A3210 - A3250.

[0051] By steps such as extracting constraint rules, analyzing the time characteristics of usage logs, triggering and executing encryption update instructions, etc., the dynamic encryption management and security verification of the signal benchmark are realized, achieving the effect of ensuring the security and reliability of the signal benchmark during the test process and preventing the test verification results from being incorrect due to the failure or tampering of the signal benchmark.

[0052] Furthermore, step A3200 in the method provided by the embodiments of the present application includes: A3210: Extract the encryption keys in the usage log sequence set to obtain a historical encryption key set.

[0053] A3220: Generate an initial encryption key using a random number generator.

[0054] A3230: Perform loss analysis on the initial encryption key and the historical encryption key set using a key security loss function to determine the initial loss amount.

[0055] A3240: Determine whether the initial loss amount satisfies the collaborative encryption asynchronous constraint. If not, perform a directional adjustment on the initial encryption key to obtain an adjusted encryption key.

[0056] A3250: When the adjusted encryption key satisfies the collaborative encryption asynchronous constraint, encrypt the adjusted encryption key using a key encryption key to obtain an updated encryption key.

[0057] In the embodiments of the present application, the loss amount refers to the quantization value obtained when performing loss analysis on the initial encryption key and the historical encryption key set using a key security loss function, and is used to judge the difference degree between the initial encryption key and the historical encryption key.

[0058] Optionally, after encrypting the update instruction upon receiving a signal reference error, first, perform a structured parsing on each usage log, extract the encrypted key fields therein (such as the log entry contains information like 2024-04-20 10:05:00, key ID: KEY001, module: A, etc.), and after removing duplicate records, store the valid encrypted keys (such as KEY001, KEY002, etc.) in an ordered set in chronological order of timestamps to obtain a historical encrypted key set. This set contains the complete version sequence of historical encrypted keys and is used for differential analysis with the newly generated initial encrypted key.

[0059] Subsequently, use a random number generator to generate an initial encrypted key, and calculate the difference value between the initial key and the historical key set through a key security loss function to obtain an initial loss amount. The specific steps are detailed in A3231 - A3232.

[0060] 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 a directional adjustment bandwidth based on the size of the initial loss amount, and randomly adjust the initial encrypted key in the direction away from the historical encrypted key set to obtain an adjusted encrypted key. The specific steps are detailed in A3241 - A3242.

[0061] Finally, when the loss amount of the adjusted encrypted key meets the loss amount threshold of the collaborative encryption asynchronous constraint, use a key encryption key mechanism (such as the RSA public key encryption algorithm) to encrypt the adjusted key: first generate an RSA public - private key pair, and use the public key to perform asymmetric encryption on the adjusted key to generate an updated encrypted key in ciphertext form. This encryption process ensures the security of key transmission and storage through the characteristics of a one - way trapdoor function in mathematics (such as the difficulty of factoring large integers). After encryption, obtain the updated updated encrypted key.

[0062] Through the full - process control of historical key analysis, dynamic generation and adjustment, mathematical model quantification, and encryption verification, the adaptive update and security enhancement of the encrypted key are realized, achieving the effects of preventing illegal tampering of signal references, improving the security of production test data, and meeting the enterprise compliance requirements.

[0063] Furthermore, step A3230 in the method provided by the embodiment of the present application includes: A3231: The key security loss function is: .

[0064] A3232: Wherein, is the initial loss amount, is the initial encrypted key, n is the total number of historical encrypted keys in the historical encrypted key set and is a positive integer, is the i-th historical encryption key in the set of historical encryption keys.

[0065] Specifically, first, a random number generator is used to generate an initial encryption key to ensure the randomness and unpredictability of the initial key. For example, a binary sequence that meets the length requirements is generated through an encryption-secure pseudo-random number generator as the initial encryption key a. Then, the key a and each historical encryption key in the set of historical encryption keys (where i ranges from 1 to n, and n is the total number of historical encryption keys and is a positive integer) are substituted into the key security loss function for calculation. Taking n = 3 as an example, if the historical encryption key = 0.2, = 0.3, = 0.4, and the generated a = 0.6, then each term is calculated respectively, and after summing, it is multiplied by to 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.

[0066] By generating an initial encryption key and using a mathematical function to calculate its difference value (initial loss amount) from the set of historical encryption keys, it lays a foundation for subsequent judgment of whether the collaborative encryption asynchronous constraint is satisfied, and further ensures the security and uniqueness of the updated encryption key. By precisely quantifying the difference between the initial encryption key and the historical key, the effect of enhancing security during the encryption key update process is achieved.

[0067] Furthermore, step A3240 in the method provided by the embodiments of the present application includes: 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 magnitude of the initial loss amount.

[0068] A3242: With the direction away from the set of historical encryption keys as the adjustment direction, randomly adjust the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key.

[0069] In the embodiments of the present application, the directional adjustment bandwidth refers to the range value for adjusting the initial encryption key determined based on the magnitude 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.

[0070] Specifically, first, a threshold judgment is made on the initial loss amount, that is, the initial loss amount is compared with the preset loss amount threshold (such as 0.8) in the collaborative encryption asynchronous constraint. If the initial loss amount is less than the threshold (for example, the calculated initial loss amount is 0.6), it indicates that the difference degree between the initial encryption key and the historical encryption key set is insufficient, and the initial encryption key needs to be adjusted to meet the security requirements.

[0071] Next, the directional adjustment bandwidth is determined based on the size of the initial loss amount to ensure that the initial loss amount is greater than or equal to the loss amount threshold in the constraint. The adjustment bandwidth has a negative correlation with the initial loss amount, that is, the larger the initial loss amount, the greater the difference between the current key and the historical key, and the smaller the required adjustment bandwidth, to ensure that the adjusted key can be significantly far from the historical key set. For example, if the initial loss amount is 0.6 (threshold is 0.8), the directional adjustment bandwidth can be set to 0.2 (the specific value is dynamically calculated according to the preset rules). The dynamic calculation of the directional adjustment bandwidth is based on the difference between the initial loss amount and the loss amount threshold in the collaborative encryption asynchronous constraint, and a quantitative mapping relationship is established through the preset rules. For example, when the initial loss amount is less than the threshold (such as the initial loss amount is 0.6 and the threshold is 0.8), the preset rule may be defined as bandwidth = threshold - initial loss amount, and at this time the directional adjustment bandwidth is 0.2; or a non-linear rule can be adopted, such as bandwidth = (threshold - initial loss amount) × dynamic coefficient (such as 1.5), so that the bandwidth increases as the difference expands, providing a quantitative adjustment range for subsequent key adjustment.

[0072] Finally, multiple stage-adjusted encryption keys are randomly adjusted from the initial encryption key according to the directional adjustment bandwidth, the loss amounts of each stage-adjusted encryption key and the historical encryption key set are calculated, and the stage-adjusted encryption key corresponding to the maximum value is extracted as the directional stage-adjusted encryption key. Taking it as the adjustment direction and adjusting the remaining stage-adjusted encryption keys according to the bandwidth to obtain the following adjustment encryption keys, and updating the directional stage-adjusted encryption key based on the following adjustment encryption keys until the preset update times are met to obtain the adjusted encryption key. The specific steps are described in detail in A3242-1-3242-5.

[0073] Through the threshold judgment of the initial loss amount and the determination of the directional adjustment bandwidth based on the judgment result, the quantitative control of the adjustment of the initial encryption key is realized, achieving the effect of maximizing the difference between the new and old keys and enhancing the security of the signal reference encryption during the key update process.

[0074] Furthermore, step A3242 in the method provided by the embodiment of the present application includes: A3242-1: Randomly adjust the initial encryption key according to the directional adjustment bandwidth to obtain multiple stage-adjusted encryption keys.

[0075] 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.

[0076] A3242-3: Extract the stage-adjusted encryption key corresponding to the maximum value from the multiple stage-adjusted loss amounts, and use it as the directional stage-adjusted encryption key.

[0077] A3242-4: Update the directional stage-adjusted encryption key to the adjustment direction, and adjust the remaining multiple stage-adjusted encryption keys according to the directional adjustment bandwidth to obtain multiple following-adjusted encryption keys.

[0078] A3242-5: Update the directional stage-adjusted encryption key based on the multiple following-adjusted encryption keys until the preset update times are met to obtain the adjusted encryption key.

[0079] Specifically, first, randomly adjust the initial encryption key according to the directional adjustment bandwidth. Generate multiple adjustment values 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, then randomly generate multiple values within the interval [0.6 - 0.3, 0.6 + 0.3], such as 0.4, 0.7, 0.9, etc.). After superimposing them on the initial encryption key, obtain multiple stage-adjusted encryption keys, and expand the key search space through randomization to increase key diversity.

[0080] Then, use the key security loss function , and calculate for each stage-adjusted encryption key and the historical encryption key set. Assume the historical encryption key set is {0.2, 0.3, 0.4}, and the stage-adjusted encryption keys are 0.4, 0.7, 0.9. Substitute them into the formula to calculate the loss amounts respectively: 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, Loss ≈ 0.95; When a = 0.9, Loss ≈ 1.64. Thus, obtain multiple stage-adjusted loss amounts, among which the maximum value is 1.64, and the corresponding stage-adjusted encryption key 0.9 is extracted as the directional stage-adjusted key and used as the current optimal adjustment direction.

[0081] Next, update the directional stage adjustment key (0.9) to the adjustment direction, and perform a secondary adjustment on the remaining stage adjustment encryption keys (0.4, 0.7) according to the directional adjustment bandwidth (0.3). For example, centered on 0.9, adjust 0.4 and 0.7 within the interval [0.9 - 0.3, 1] to obtain multiple following adjustment encryption keys (such as 0.6, 0.8). Among them, the key values in the key security loss function must be within the interval [0, 1].

[0082] Finally, use the key security loss function to calculate the loss amounts of multiple following adjustment encryption keys and the historical encryption key set. If there is a following adjustment loss amount greater than or equal to the loss amount corresponding to the directional stage adjustment encryption key, update the following adjustment encryption key corresponding to the maximum value to the directional stage adjustment encryption key, and then based on this, adjust the remaining following adjustment encryption keys according to the directional adjustment bandwidth until the preset update times are met to obtain the adjusted encryption key. The specific steps are described in detail in A3242 - 5A - 3242 - 5B.

[0083] The loop process of generating candidate keys through random adjustment, quantifying losses to screen the optimal direction, and iteratively updating the adjustment direction realizes the directional optimization update of the encryption key, achieving the effect of maximizing the difference between the key and the historical set within a limited number of iterations and strengthening the signal benchmark encryption security.

[0084] Furthermore, step A342 - 5 in the method provided by the embodiment of the present application includes: A3242 - 5A: Use the key security loss function to calculate the loss amounts of the multiple following adjustment encryption keys and the historical encryption key set, and obtain multiple following adjustment loss amounts.

[0085] 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, update the following adjustment encryption key corresponding to the maximum value among the multiple following adjustment loss amounts to the directional stage adjustment encryption key, and again perform a directional adjustment on the remaining multiple following adjustment encryption keys according to the directional adjustment bandwidth based on the directional stage adjustment encryption key until the preset update times are met to obtain the adjusted encryption key.

[0086] In one embodiment, first, use the key security loss function , substitute and calculate each of the multiple following adjustment encryption keys and the historical encryption key set one by one to obtain the following adjustment loss amount corresponding to each following adjustment encryption key. For example, assume that the historical encryption key set is {0.2, 0.3, 0.4}, the directional stage adjustment encryption key is 0.9 (corresponding loss amount 1.64), and the following 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.

[0087] Then, compare the follow - up adjustment loss with the stage adjustment loss (1.64) of the current directional - phase adjustment encryption key. If there is a follow - up adjustment loss greater than or equal to this value (assuming the follow - up adjustment key loss in another round of adjustment is 2.11), then update the follow - up adjustment encryption key corresponding to the maximum value (such as the key 0.95 corresponding to 2.11) to the new directional - phase adjustment encryption key. Then, based on the new direction, re - adjust the remaining follow - up adjustment encryption keys according to the directional adjustment bandwidth (such as 0.95). For example, adjust the original follow - up adjustment keys 0.6 and 0.8 to 0.2 and 0.6 (centered on the new direction 0.95, with a bandwidth of ±0.95, which means adjusting the follow - up adjustment encryption keys within the range of [0, 1] with 0.95 as the reference value). Repeat the calculation of the loss and update the direction until the preset number of updates is reached. The preset number of updates should ensure that the loss approaches the threshold of the collaborative encryption asynchronous constraint in the iteration. If the loss is close to the threshold after the first iteration, a smaller number (such as 3 - 5 times) can be set; if the convergence is slow, the number of times needs to be increased until the loss is stable and meets the standard, thereby obtaining the adjusted encryption key.

[0088] Through the loop mechanism of loss calculation, direction evaluation, and iterative adjustment, the adaptive iterative update of the encryption key is realized, achieving the effect of maximizing the difference between the key and the historical set within a limited number of times, significantly enhancing the signal - based encryption security and anti - tampering ability.

[0089] Furthermore, step A5000 in the method provided by the embodiment of the present application includes: A5100: Extract the historical test log sequence from the window backtracking unit.

[0090] A5200: Extract the related words of the first historical test log in the historical test log sequence according to the preset abnormal related - word set, and add the extraction result into the empty vector to obtain the first memory.

[0091] A5300: Extract the related words of the second historical test log in the historical test log sequence based on the preset abnormal related - word and the first memory, and update the first memory according to the extraction result to obtain the second memory.

[0092] A5400: And so on until the end of the historical test log sequence is reached to obtain a time-series iterative memory.

[0093] A5500: Use an anomaly information extractor to identify the time-series iterative memory and obtain anomaly information.

[0094] In the embodiments of the present application, the anomaly correlation words are a preset keyword set for identifying anomaly information in production test logs. The time-series iterative memory is a time-series correlation memory vector constructed by iteratively processing the historical test log sequence. The anomaly information extractor is a tool for analyzing and identifying the time-series iterative memory.

[0095] Optionally, first, extract the historical test log sequence from the window backtracking unit of the cloud data platform, and this sequence contains production test process records arranged in chronological order (such as test time, test item results, signal reference values, etc.).

[0096] Next, disassemble the first historical test log at the word level to generate a word sequence set, traverse this set and perform semantic similarity matching with the preset anomaly correlation word set, and add the successfully matched correlation words into an empty vector to obtain the first memory. The specific steps are described in detail in A5210 - A5220.

[0097] Then, based on the preset anomaly correlation word set and the first memory, process the second historical test log: In addition to matching the preset keywords, it is also necessary to perform extended matching in combination with the correlation words in the first memory (such as the voltage value exceeding the limit in the first log). If the second log shows an abnormal current value, where the anomaly belongs to the preset word and there is a semantic association between the current value and the voltage value in the first memory (both are physical quantity parameters), then add the abnormal current value to the vector and update the first memory to the second memory containing the new keywords. And so on. Each time a log is processed, it is iteratively updated based on the previous memory until all logs are processed to form a time-series iterative memory containing time-series correlation information.

[0098] Finally, when using the anomaly information extractor to analyze the time-series iterative memory, first, the historical test log sequence is disassembled line by line into a set of word sequences through a preset set of anomaly-related words. After semantic similarity matching, memory vectors containing time-series associations are generated (such as the first memory, the second memory, etc., until a complete time-series iterative memory is formed). On this basis, the association rule mining algorithm (such as the Apriori algorithm) is used to analyze the keyword occurrence frequency and time-series relationship in the memory vector, and support threshold (such as at least 20% of the logs contain a certain keyword combination) and confidence threshold (such as the probability of triggering an anomaly when a certain combination appears ≥ 80%) are set to identify high-frequency associated anomaly patterns. For example, when the voltage value exceeds the limit and the current value is abnormal continuously appear in the time-series memory and the frequency of accompanying test failures exceeds the preset threshold, the association rule engine triggers an anomaly determination, maps it to the power module anomaly type in combination with the production test business logic, and finally outputs specific anomaly information and associated factors.

[0099] Through the construction of time-series iterative memory and semantic association analysis of historical test logs, the goal of accurately extracting anomaly information from a large number of logs is achieved, and the effect of improving the anomaly location efficiency in the production test process and strengthening the data security monitoring ability is achieved.

[0100] Furthermore, step A5200 in the method provided by the embodiments of the present application includes: A5210: Disassemble the first historical test log at the word level to generate a set of word sequences.

[0101] A5220: Traverse the set of word sequences and perform semantic similarity matching with the preset set of anomaly-related words. If the match is successful, add it as a related word to the empty vector to obtain the first memory.

[0102] In one embodiment, first, the first historical test log is disassembled at the word level, and the log text content is segmented into independent word units to generate a set of word sequences. For example, assume that the first historical test log is that the voltage value of the power module exceeds the limit and the test fails. After disassembly, a set of word sequences [power supply, module, voltage value, exceeds the limit, test, fails] is formed.

[0103] Next, traverse each word in the word sequence set and perform semantic similarity matching with the preset abnormal correlation word set. The preset abnormal correlation word set contains common abnormal description words in the industry, such as [overlimit, abnormal, failure, error, interruption], etc. Use the cosine similarity algorithm to calculate the semantic similarity between words, and set a matching threshold (such as similarity ≥ 0.7). If the word in the word sequence matches a certain correlation word in the preset set successfully, add it to the empty vector. For example, the words overlimit and failure in the word sequence are exactly matched with overlimit and failure in the preset set, and the voltage value is filtered because there is no direct matching item in the preset set. Finally, the first memory vector containing [overlimit, failure] is generated.

[0104] Furthermore, when calculating the semantic similarity between words through the cosine similarity algorithm, first disassemble the first historical test log at the word level to generate a word sequence set (such as disassembling voltage value overlimit into voltage value, overlimit), then convert each word into a vector representation (such as generating word vectors through Word2Vec), and then calculate the cosine value of the angle between the word vectors. If the value ≥ 0.7 (such as the cosine value of overlimit and the preset word abnormal is 0.8), it is determined that the match is successful. The words that match successfully are added to the empty vector to form the first memory.

[0105] Through the word-level disassembling, semantic similarity matching, and associated vector construction of the historical test log, the goal of efficiently extracting abnormal-related features from unstructured log data is achieved, and the effect of providing structured input for time-series iterative memory analysis and improving the efficiency of production test anomaly location is achieved.

[0106] In summary, the FCT security collaborative scheduling method based on full-link data fusion provided by the embodiments of this application has the following technical effects: In this application, the production test plan is parsed through the cloud data platform to obtain the production test item sequence and instruction sequence, which are sent to the FCT client to match the responsible chained execution engine. The production test items are tested based on the execution engine sequence and production test instructions. The signal reference security is verified through the full-link staggered time collaborative encryption verification strategy to generate the test verification result. If the test passes, the log is stored in partitions 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 collaboratively schedule the execution engine. At the same time, the minimum value of the time stamp is extracted from the signal reference usage log 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 reference security verification, so as to accurately perform production test scheduling and data security protection, making the security collaboration and data scheduling in the FCT production test process more accurate and reliable, achieving the technical effects of using the security collaboration mechanism to realize the differentiated management of factory data and the tampering prevention and control of signal references, combining the full-link data scheduling to optimize the test process, and improving data security, management isolation, and enterprise compliance.

[0107] Embodiment 2. As Figure 2 shown, based on the same inventive concept as in the foregoing Embodiment 1, the embodiment of the present application provides an FCT secure collaborative scheduling system based on full-link data fusion. The system includes: A production test sequence acquisition module 1, which is configured to use a cloud data platform to parse a production test plan according to a target test product and test requirements, and obtain a production test item sequence and a production test instruction sequence.

[0108] An engine sequence acquisition module 2, which is configured to send the production test item sequence and the production test instruction sequence to an FCT client, and use the FCT client to perform a responsible chain execution engine matching to obtain an execution engine sequence.

[0109] A test result generation module 3, which is configured to sequentially test the production test item sequence according to the production test instruction sequence based on the execution engine sequence, and perform security verification on the corresponding signal reference through a full-link staggered-time collaborative encryption verification strategy during the test. If the verification is successful, use the signal reference for test verification to generate a test verification result.

[0110] A test execution module 4, which is configured to upload a test log to the cloud data platform for partition storage when the test verification result is that the production test item passes, and execute the test of the next production test item in the order from front to back of the production test item sequence until all production test items are tested.

[0111] A collaborative scheduling execution module 5, which is configured to upload a test log to the cloud data platform for partition storage when the test verification result is that the production test item fails, and simultaneously perform sequential iterative memory analysis on the data stored in the window backtracking unit in the cloud data platform to determine abnormal information, and perform secure collaborative scheduling on the corresponding execution engine based on the abnormal information.

[0112] Further, the test result generation module 3 is configured to perform the following steps: Extract the signal reference mis-timing encryption update constraint and the collaborative encryption asynchrony constraint in the full-link mis-timing collaborative encryption verification policy; obtain the set of usage log sequences of the full-link signal reference of the production test item sequence, analyze the minimum value from the timestamp of the usage log in the set of usage log sequences to the current time window, and determine whether the minimum value satisfies the signal reference mis-timing encryption update constraint; if so, perform security verification based on the encryption key of the signal reference; if not, trigger a signal reference mis-timing encryption update instruction, and according to the signal reference mis-timing encryption update instruction, combine the set of usage log sequences and the collaborative encryption asynchrony constraint to update the encryption key of the signal reference, and perform security verification of the signal reference based on the updated encryption key.

[0113] Further, the test result generation module 3 is used to perform the following steps: Extract the encryption keys in the set of usage log sequences, and obtain a set of historical encryption keys; generate an initial encryption key using a random number generator; perform loss analysis on the initial encryption key and the set of historical encryption keys using a key security loss function to determine the initial loss amount; determine whether the initial loss amount satisfies the collaborative encryption asynchrony constraint, if not, perform directional adjustment on the initial encryption key to obtain an adjusted encryption key; when the adjusted encryption key satisfies the collaborative encryption asynchrony constraint, encrypt the adjusted encryption key using a key encryption key to obtain an updated encryption key.

[0114] Further, the test result generation module 3 is used to perform the following steps: The key security loss function is: ; where is the initial loss amount, is the initial encryption key, n is the total number of historical encryption keys in the set of historical encryption keys and is a positive integer, is the i-th historical encryption key in the set of historical encryption keys.

[0115] Further, the test result generation module 3 is used to perform the following steps: Determine whether the initial loss amount is greater than or equal to the loss amount threshold in the collaborative encryption asynchrony constraint, if not, determine the directional adjustment bandwidth based on the magnitude of the initial loss amount; with the direction of moving away from the set of historical encryption keys as the adjustment direction, perform random adjustment on the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key.

[0116] Further, the test result generation module 3 is used to perform the following steps: Randomly adjust the initial encryption key according to the described directionality to obtain multiple stage-adjusted encryption keys; use the key security loss function to calculate the loss amounts between the multiple stage-adjusted encryption keys and the historical encryption key set iteratively, obtaining multiple stage-adjusted loss amounts; extract the stage-adjusted encryption key corresponding to the maximum value from the multiple stage-adjusted loss amounts, and use it as the directionality stage-adjusted encryption key; update the directionality stage-adjusted encryption key as the adjustment direction, and adjust the remaining multiple stage-adjusted encryption keys according to the directionality adjustment bandwidth to obtain multiple following-adjusted encryption keys; update the directionality stage-adjusted encryption key based on the multiple following-adjusted encryption keys until the preset number of updates is satisfied, obtaining the adjusted encryption key.

[0117] Further, the test result generation module 3 is used to perform the following steps: Calculate the loss amounts between the multiple following-adjusted encryption keys and the historical encryption key set by using the key security loss function, obtaining multiple following-adjusted loss amounts; when there is a following-adjusted loss amount in the multiple following-adjusted loss amounts that is greater than or equal to the stage-adjusted loss amount corresponding to the directionality stage-adjusted encryption key, update the following-adjusted encryption key corresponding to the maximum value in the multiple following-adjusted loss amounts as the directionality stage-adjusted encryption key, and again perform directionality adjustment on the remaining multiple following-adjusted encryption keys according to the directionality adjustment bandwidth based on the directionality stage-adjusted encryption key until the preset number of updates is satisfied, obtaining the adjusted encryption key.

[0118] Further, the cooperative scheduling execution module 5 is used to perform the following steps: Extract the historical test log sequence from the window backtracking unit; extract the related words from the first historical test log in the historical test log sequence according to the preset abnormal related word set, and add the extraction result to an empty vector to obtain the first memory; extract the related words from the second historical test log in the historical test log sequence based on the preset abnormal related words and the first memory, and update the first memory according to the extraction result to obtain the second memory; and so on until the end of the historical test log sequence is reached, obtaining the time-sequential iterative memory; use the abnormal information extractor to identify the abnormal information from the time-sequential iterative memory.

[0119] Further, the cooperative scheduling execution module 5 is used to perform the following steps: Decompose the first historical test log at the word level to generate a word sequence set; iterate through the word sequence set to perform semantic similarity matching with the preset abnormal related word set, and if the match is successful, add it as a related word to an empty vector to obtain the first memory.

[0120] The FCT security collaborative scheduling system based on full-link data fusion provided by the embodiments of the present invention can execute the FCT security collaborative scheduling method based on full-link data fusion provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0121] Although this application makes various references to certain modules in the system according to the embodiments of this application, however, any number of different modules can be used and run on the user terminal and / or server. 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 mutual distinction and do not limit the protection scope of the present invention.

[0122] The above specific implementation manners do not constitute a limitation to the protection scope of this application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this application shall be included within the protection scope of this application. In some cases, the actions or steps recorded in this application can be executed in a different order from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. FCT security collaborative scheduling method based on full-link data fusion, characterized in that The method includes: using a cloud data platform to parse a production test plan according to a target test product and test requirements, obtaining a production test item sequence and a production test instruction sequence; sending the production test item sequence and the production test instruction sequence to an FCT client, and using the FCT client to perform a responsible chain execution engine matching to obtain an execution engine sequence; based on the execution engine sequence, sequentially testing the production test item sequence according to the production test instruction sequence, and performing security verification on corresponding signal references through a full-link misaligned collaborative encryption verification strategy during the test. If the verification is successful, use the signal reference to perform a test verification and generate a test verification result; when the test verification result indicates that a production test item passes, upload the test log to the cloud data platform for partition storage, and execute the test of the next production test item in the order from front to back of the production test item sequence until all production test items are tested; when the test verification result indicates that a production test item fails, upload the test log to the cloud data platform for partition storage, and simultaneously perform a timing iterative memory analysis on the data stored in the window backtracking unit in the cloud data platform to determine abnormal information, and perform a secure collaborative scheduling on the corresponding execution engine based on the abnormal information; wherein, when sequentially testing the production test item sequence according to the production test instruction sequence based on the execution engine sequence, performing security verification on corresponding signal references through a full-link misaligned collaborative encryption verification strategy, and if the verification is successful, performing a test verification based on the verified signal reference to generate a test verification result, including: extracting the signal reference misaligned encryption update constraint and the collaborative encryption asynchronous constraint in the full-link misaligned collaborative encryption verification strategy; obtaining a usage log sequence set of the full-link signal reference 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 determining whether the minimum value satisfies the signal reference misaligned encryption update constraint; if so, performing security verification based on the encryption key of the signal reference; if not, triggering a signal reference misaligned encryption update instruction, and according to the signal reference misaligned encryption update instruction, combining the usage log sequence set and the collaborative encryption asynchronous constraint to update the encryption key of the signal reference, and performing security verification of the signal reference based on the updated encryption key.

2. The FCT security collaborative scheduling method based on full-link data fusion according to claim 1, wherein According to the signal reference misaligned encryption update instruction, combining the usage log sequence set and the collaborative encryption asynchronous constraint to update the encryption key of the signal reference, and performing security verification of the signal reference based on the updated encryption key, including: Extracting the encryption key in the usage log sequence set to obtain a historical encryption key set; Using a random number generator to generate an initial encryption key; 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, and if not, performing a directional adjustment on the initial encryption key to obtain an adjusted encryption key; When the adjusted encryption key satisfies the collaborative encryption asynchronous constraint, encrypt the adjusted encryption key using the key encryption key to obtain an updated updated encryption key.

3. The FCT security collaborative scheduling method based on full-link data fusion according to claim 2, wherein The key security loss function is: ； 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.

4. The FCT security collaborative scheduling method based on full-link data fusion according to claim 2, characterized in that, Determine whether the initial loss amount satisfies the collaborative encryption asynchronous constraint. If not, perform a directional adjustment on the initial encryption key to obtain an adjusted encryption key, including: 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 magnitude of the initial loss amount; Taking the direction away from the historical encryption key set as the adjustment direction, randomly adjust the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key.

5. The FCT security collaborative scheduling method based on full-link data fusion according to claim 4, wherein, Taking the direction away from the historical encryption key set as the adjustment direction, randomly adjust the initial encryption key based on the directional adjustment bandwidth to obtain the adjusted encryption key, including: Randomly adjust the initial encryption key according to the directional adjustment bandwidth to obtain multiple stage-adjusted encryption keys; Using the key security loss function, traverse and calculate the loss amounts between the multiple stage-adjusted encryption keys and the historical encryption key set to obtain multiple stage-adjusted loss amounts; Extract the stage-adjusted encryption key corresponding to the maximum value from the multiple stage-adjusted loss amounts, and use it as the directional stage-adjusted encryption key; Update the directional stage-adjusted encryption key as the adjustment direction, and adjust the remaining multiple stage-adjusted encryption keys according to the directional adjustment bandwidth to obtain multiple following-adjusted encryption keys; Update the directional stage-adjusted encryption key based on the multiple following-adjusted encryption keys until the preset update times are satisfied to obtain the adjusted encryption key.

6. The FCT security collaborative scheduling method based on full-link data fusion according to claim 5, characterized in that, Update the directional stage-adjusted encryption key based on the multiple following-adjusted encryption keys until the preset update times are satisfied to obtain the adjusted encryption key, including: Use the key security loss function to calculate the loss amounts between the multiple following-adjusted encryption keys and the historical encryption key set to obtain multiple following-adjusted loss amounts; When there is a following-adjusted loss amount greater than or equal to the stage-adjusted loss amount corresponding to the directional stage-adjusted encryption key among the multiple following-adjusted loss amounts, update the following-adjusted encryption key corresponding to the maximum value among the multiple following-adjusted loss amounts as the directional stage-adjusted encryption key, and then perform a directional adjustment on the remaining multiple following-adjusted encryption keys according to the directional adjustment bandwidth based on the directional stage-adjusted encryption key until the preset update times are satisfied to obtain the adjusted encryption key.

7. The FCT security collaborative scheduling method based on full-link data fusion according to claim 1, characterized in that Including: Extract the historical test log sequence from the window backtracking unit; Extract the related words from the first historical test log in the historical test log sequence according to the preset abnormal related word set, and add the extraction result to the empty vector to obtain the first memory; Extract the related words from the second historical test log in the historical test log sequence based on the preset abnormal related words and the first memory, and update the first memory according to the extraction result to obtain the second memory; And so on until the end of the historical test log sequence is reached, obtaining a time-series iterative memory; An anomaly information extractor is used to identify the time-series iterative memory to obtain anomaly information.

8. The FCT security collaborative scheduling method based on full-link data fusion according to claim 7, wherein, According to a preset set of anomaly-related words, the related words of the first historical test log in the historical test log sequence are extracted, and the extraction result is added to an empty vector to obtain a first memory, including: The first historical test log is disassembled at the word level to generate a set of word sequences; Traverse the set of word sequences and the preset set of anomaly-related words for semantic similarity matching. If the match is successful, it is added to the empty vector as a related word to obtain the first memory.

9. The FCT security collaborative scheduling system based on full-link data fusion is characterized in that For implementing the FCT security collaborative scheduling method based on full-link data fusion according to any one of claims 1-8, the system includes: A production test sequence acquisition module, configured to use a cloud data platform to parse a production test plan according to a target test product and test requirements, and obtain a production test item sequence and a production test instruction sequence; An engine sequence acquisition module, configured to send the production test item sequence and the production test instruction sequence to an FCT client, and use the FCT client to perform a responsible chain execution engine matching to obtain an execution engine sequence; A test result generation module, based on the execution engine sequence, sequentially tests the production test item sequence according to the production test instruction sequence, and performs security verification on the corresponding signal reference through a full-link time-offset collaborative encryption verification strategy during the test. If the verification is successful, the signal reference is used for test verification to generate a test verification result; A test execution module, configured to upload the test log to the cloud data platform for partition storage when the test verification result indicates that the production test item passes, and execute the test of the next production test item in the order from front to back of the production test item sequence until all production test items are tested; A collaborative scheduling execution module, configured to upload the test log to the cloud data platform for partition storage when the test verification result indicates 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 anomaly information, and perform secure collaborative scheduling on the corresponding execution engine based on the anomaly information.

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