Joint verification method and system for data integrity of trusted data space

Abstract

The invention relates to a data integrity joint verification method and system for a trusted data space, and relates to the technical field of industrial data verification, and the method comprises the steps: taking the data importance, the Hash calculation frequency and the uplink evidence storage frequency in a preset time zone as an adaptive data integrity verification strategy; processing a real-time operation state data stream of the industrial equipment, generating a data integrity verification certificate, and storing the certificate to a credible data space distributed account book; when a maintenance analysis request is initiated, performing integrity verification on the obtained original operation state data stream based on a data integrity verification voucher on the distributed account book; and if the verification is passed, performing equipment health state diagnosis and targeted maintenance according to the original operation state data stream. The method solves the problems that a conventional data integrity verification method mostly adopts a fixed verification strategy, is not adaptive to data importance and network states, and cannot meet the requirements of predictive maintenance on efficiency and real-time performance while guaranteeing data credibility.

Description

Technical Field

[0001] This application relates to the field of industrial data verification, and in particular to a joint verification method and system for data integrity in a trusted data space. Background Technology

[0002] As the industrial manufacturing sector increasingly relies on predictive maintenance for equipment, the integrity and verification efficiency of real-time operating status data for industrial equipment have become key technological requirements for supporting equipment health diagnosis and reducing the risk of failure.

[0003] Currently, traditional data integrity verification methods mostly adopt fixed verification strategies, which are not dynamically adjusted according to the differences in data importance and real-time network status in industrial scenarios. This not only makes it difficult to adapt to the credibility requirements of different monitoring indicators, but also easily leads to redundant storage, computing overhead and network latency due to indiscriminate verification. It cannot meet the requirements of predictive maintenance for data processing efficiency and real-time performance while ensuring data credibility, and increases the risk of misjudgment in equipment maintenance and delayed fault warning. Summary of the Invention

[0004] This application provides a joint verification method and system for data integrity in a trusted data space, which improves upon the shortcomings of traditional data integrity verification methods that often employ fixed verification strategies, fail to adapt to data importance and network conditions, and cannot meet the current requirements of efficiency and real-time performance for predictive maintenance of industrial equipment while ensuring data trustworthiness.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for joint verification of data integrity in a trusted data space, the method comprising:

[0007] The hash calculation frequency and on-chain evidence storage frequency are set based on the data importance in the preset time zone and the real-time network status as an adaptation data integrity verification strategy.

[0008] On the edge computing node local to the factory, the real-time operating status data stream of industrial equipment is processed according to the adapted data integrity verification strategy, a data integrity verification certificate is generated, and submitted to the distributed ledger in the trusted data space for storage.

[0009] When the equipment maintenance service provider initiates a maintenance analysis request, it obtains the original operating status data stream of the edge computing node within the preset time zone, and performs integrity verification on the original operating status data stream based on the data integrity verification certificate on the distributed ledger.

[0010] If the data integrity verification passes, the device health status diagnosis and targeted maintenance are performed based on the original operating status data stream.

[0011] Secondly, embodiments of this application provide a joint data integrity verification system for a trusted data space, the system comprising:

[0012] The verification strategy setting module is used to set the hash calculation frequency and the on-chain evidence storage frequency based on the data importance in the preset time zone and the real-time network status, as an adaptation to the data integrity verification strategy.

[0013] The certificate generation and storage module is used to process the real-time operating status data stream of industrial equipment on the local edge computing node of the factory according to the adapted data integrity verification strategy, generate data integrity verification certificates, and submit them to the distributed ledger in the trusted data space for storage.

[0014] The data integrity verification module is used to obtain the original operating status data stream of the edge computing node in the preset time zone when the equipment maintenance service provider initiates a maintenance analysis request, and to perform integrity verification on the original operating status data stream based on the data integrity verification certificate on the distributed ledger.

[0015] The equipment diagnosis and maintenance module is used to perform equipment health status diagnosis and targeted maintenance based on the original operating status data stream if the data integrity verification passes.

[0016] Thirdly, embodiments of this application provide a computer-readable storage medium storing a first computer program, which, when executed by a processor, implements a data integrity joint verification method for a trusted data space according to the first aspect.

[0017] Fourthly, embodiments of this application provide a verification terminal, the verification terminal comprising:

[0018] Memory, used to store a second computer program;

[0019] A processor is used to read and execute the second computer program, thereby implementing a joint verification method for data integrity in a trusted data space, as described in the first aspect.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] This application proposes a joint data integrity verification method and system for a trusted data space. Through the collaborative operation of adaptive verification strategy setting, real-time data stream processing and credential storage, data integrity verification, and equipment health diagnosis and maintenance, it achieves accurate protection and efficient application of data integrity in the predictive maintenance process of industrial equipment. First, based on the production plan within the factory's preset time zone, the expected production tasks and product demands of the target industrial equipment are screened. Predicted operating load sequences are obtained through equipment operation load simulation. The importance of equipment monitoring indicators is analyzed using a data importance evaluation rule base, outputting a data importance set. Then, considering production load volatility and real-time network communication performance parameters, the hash calculation frequency and on-chain notarization frequency are optimized to form an adaptive data integrity verification strategy. Subsequently, on the factory's local edge computing node, the real-time operating status data stream of the industrial equipment is filtered, sampled, and grouped. A cryptographic hash algorithm is used to generate hash values ​​and construct a Merkle tree. The root hash value is used as the data integrity verification credential, packaged with time window information, equipment identifiers, etc., into a notarized transaction, and broadcast to the distributed ledger in the trusted data space for notarization. When an equipment maintenance service provider initiates a maintenance analysis request, the original operating status data stream is obtained from the edge computing node, the hash value is recalculated, and the Merkle tree is reconstructed. This is compared with the verification credential queried in the distributed ledger to complete integrity verification. Finally, if the verification passes, equipment health status diagnosis is conducted based on the original data stream, and a targeted maintenance plan is formulated and executed.

[0022] The technical solution of this application solves the problems in traditional industrial data verification, such as the inability of fixed strategies to match differences in data importance and changes in network status, insufficient credibility of data evidence, and difficulty in ensuring the authenticity of maintenance and analysis data. It realizes the integrity guarantee of industrial equipment data from collection and processing to application and maintenance, and improves the accuracy and reliability of predictive maintenance of industrial equipment. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a joint verification method for data integrity in a trusted data space, provided in an embodiment of this application;

[0025] Figure 2 A schematic diagram of the structure of a joint data integrity verification system for a trusted data space provided in this application embodiment;

[0026] Figure 3This is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of the verification terminal provided in an embodiment of this application.

[0028] The components represented by each number in the attached diagram are explained below:

[0029] Verification strategy setting module 01, voucher generation and storage module 02, data integrity verification module 03, equipment diagnosis and maintenance module 04, computer-readable storage medium 300, first computer program 311, verification terminal 400, memory 410, processor 420, and second computer program 411. Detailed Implementation

[0030] This application provides a data integrity joint verification method and system for a trusted data space, which addresses the technical problems of existing data integrity verification methods that often employ fixed verification strategies and fail to adapt to the importance of data and real-time network status in industrial scenarios. This makes it difficult to balance data reliability and processing efficiency, and fails to meet the requirements of predictive maintenance of industrial equipment for data integrity assurance, real-time performance, and low overhead.

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0033] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0034] Example 1, as shown in the appendix Figure 1 As shown, this application provides a joint verification method for data integrity in a trusted data space, the method comprising the following steps:

[0035] S110: Set the hash calculation frequency and on-chain evidence storage frequency based on the data importance in the preset time zone and the real-time network status, as an adaptation data integrity verification strategy.

[0036] In this embodiment of the application, in the scenario of predictive maintenance of industrial equipment, in order to avoid the waste of resources or insufficient verification caused by traditional fixed verification strategies, and to ensure that data integrity verification can match the actual value of industrial data and adapt to the real-time network carrying capacity, it is necessary to dynamically formulate verification strategies based on the production situation and network status of the preset time zone in order to improve the pertinence and efficiency of data verification.

[0037] Specifically, based on the factory's production plan within a preset time zone, the expected production tasks and expected product demands of the target industrial equipment to be maintained and analyzed are first selected.

[0038] Furthermore, based on the expected production tasks, the target industrial equipment is subjected to equipment operation load simulation. The digital twin model of the target industrial equipment is called, and the expected production tasks are input into the digital twin model to carry out operation simulation. During the simulation, the load rate of the key subsystems of the target industrial equipment is calculated in real time, and the load rate of the key subsystems is output in time sequence to form a predicted operation load sequence.

[0039] Furthermore, based on the expected product demand and predicted operating load sequence, the importance of several monitoring indicators for the target industrial equipment is evaluated. The impact of each monitoring indicator on the equipment operating status assessment under different product demands and the criticality of the indicators when the load changes are comprehensively considered, and finally, a set of data importance is output.

[0040] Furthermore, by combining real-time network status, the network communication between edge computing nodes and trusted data space is analyzed, and the frequency of hash calculation and the interval of on-chain evidence storage are dynamically adjusted. The set hash calculation frequency and on-chain evidence storage frequency are integrated into an adaptive data integrity verification strategy.

[0041] This step, through comprehensive consideration of everything from production planning to network status, ensures that the data integrity verification strategy can focus on the effective verification of critical data while avoiding the ineffective use of network resources, providing a suitable execution basis for the subsequent processing of real-time operational status data streams, the generation and storage of verification credentials.

[0042] Step S110 in the method provided in this application embodiment includes:

[0043] Based on the factory's production plan within the preset time zone, the expected production tasks and expected product demands of the target industrial equipment are obtained by screening.

[0044] Based on the expected production tasks, the target industrial equipment is simulated for equipment operation load to obtain the predicted operation load sequence;

[0045] Based on the expected product demand and predicted operating load sequence, the data importance of several monitoring indicators of the target industrial equipment is evaluated, and a data importance set is output.

[0046] The hash calculation frequency and on-chain evidence storage frequency are set based on the data importance set and real-time network status as an adaptation data integrity verification strategy.

[0047] In this embodiment of the application, in order to avoid the problem that traditional verification strategies cannot adapt to the differences in data value and changes in network status in the predictive maintenance scenario of industrial equipment, it is necessary to gradually derive an appropriate data integrity verification strategy by combining factory production plans, equipment operating characteristics, data importance and real-time network status, so as to improve the accuracy of data verification and the applicability of industrial scenarios.

[0048] Specifically, based on the factory's production plan within a preset time zone, the expected production tasks and expected product demands of the target industrial equipment are first screened out.

[0049] Among them, the target industrial equipment is the equipment to be maintained and analyzed. The screening process needs to accurately match the production arrangements related to the equipment in the production plan to ensure that the expected production tasks obtained can reflect the actual operating needs of the equipment in the preset time zone, and the expected product demand can reflect the quality and specification requirements of the equipment's production output, thus laying the foundation for subsequent equipment operating load analysis.

[0050] Furthermore, based on the expected production tasks, the target industrial equipment is subjected to equipment operation load simulation to obtain the predicted operation load sequence.

[0051] Specifically, during simulation, the digital twin model of the target industrial equipment must first be called. This digital twin model can accurately replicate the physical structure, operating mechanism and subsystem relationships of the equipment. Then, the selected expected production tasks are input into the digital twin model to carry out the operation simulation.

[0052] During the simulation, the load rate of the key subsystems of the target industrial equipment needs to be calculated in real time. The selection of key subsystems needs to be determined in combination with the equipment type and production task characteristics. For example, the key subsystems of machine tool equipment may include the spindle drive system, feed system, etc.

[0053] Finally, through simulation using a digital twin model, the load rate of each key subsystem is obtained in real time, and then the load rates of each key subsystem are output sequentially in chronological order to form a predicted operating load sequence.

[0054] Furthermore, after obtaining the expected product demand and the predicted operating load sequence, it is necessary to evaluate the data importance of several monitoring indicators of the target industrial equipment in order to output the data importance set.

[0055] The method provided in this application embodiment, which "evaluates the data importance of several monitoring indicators of the target industrial equipment based on the expected product demand and predicted operating load sequence, and outputs a data importance set", includes:

[0056] A data importance evaluation rule base is constructed, wherein the data importance evaluation rule base defines the importance weights of several monitoring indicators under different product demand and load levels;

[0057] Traverse the predicted operating load sequence, and for the load value at each time point, query the data importance evaluation rule base in conjunction with the expected product demand to obtain a set of importance scores for several monitoring indicators;

[0058] The importance score set is aggregated and calculated to generate several data importance scores for several monitoring indicators, and a data importance set is constructed.

[0059] In this embodiment of the application, in order to avoid the problem that key data is not given priority and non-key data occupies too much verification resources due to the use of a uniform evaluation standard for all monitoring indicators of industrial equipment, it is necessary to evaluate the data importance of monitoring indicators step by step in combination with expected product demand and equipment operating load characteristics, so as to generate a data importance set that can reflect the difference in the value of indicators, and provide a basis for setting the subsequent hash calculation frequency and on-chain evidence storage frequency.

[0060] Specifically, the first step is to construct a data importance evaluation rule base. The monitoring indicators need to be set based on the type of industrial equipment. For example, for CNC machine tools, monitoring indicators may include spindle speed, cutting force, and guideway temperature; for industrial robots, monitoring indicators may include joint torque, motion accuracy, and motor temperature.

[0061] In addition, the importance weights of monitoring indicators defined in the data importance evaluation rule base under different product demand and load levels need to be determined in combination with industrial production practice experience and equipment failure impact analysis.

[0062] For example, when producing high-precision parts (high product demand) and the equipment is operating under high load, the weight of monitoring indicators directly related to processing accuracy should be set higher, while the weight of monitoring indicators with low correlation to the core functions of the equipment should be relatively lower, so as to ensure that the weight allocation can truly reflect the degree of influence of the indicators on the health status and production quality of the equipment in different scenarios.

[0063] Furthermore, after completing the construction of the data importance evaluation rule base, the obtained predicted operating load sequence is traversed, and for the load value at each time point, the rule base is queried in combination with the expected product demand to obtain a set of importance scores for several monitoring indicators.

[0064] Specifically, the traversal process requires analyzing each time point in the predicted operating load sequence one by one. The load value at each time point represents the operating load level of the equipment at that moment. Combined with the expected product demand at that time, the key requirements for equipment operation at that time point can be accurately located.

[0065] For example, at a certain point in time, the equipment load is 90% of the rated load (high load), and the expected product is precision instrument parts (high product demand). At this time, when querying the data importance evaluation rule base, the monitoring indicators related to equipment accuracy and core component load will receive higher importance scores, while the monitoring indicators related to auxiliary functions will receive relatively lower scores. Each point in time will form a set of monitoring indicator importance scores corresponding to the current scenario.

[0066] Finally, the importance scores for all time points are aggregated and calculated to generate the data importance of several monitoring indicators, and a data importance set is constructed accordingly.

[0067] Specifically, aggregated computing needs to comprehensively consider the correlation between load characteristics and product demand at different time points, and adopt a weighted average calculation method to give higher weight to time points with drastic load fluctuations and critical product demand, so as to ensure that the importance of indicators in these key scenarios accounts for a higher proportion in the final result.

[0068] For example, within a preset time zone, different stages such as equipment startup, high-load operation, and shutdown can be covered. Higher weight can be assigned to the time points of the high-load operation stage, so that the importance of the final calculated monitoring index data is more in line with the needs of the core operating scenarios of the equipment.

[0069] Furthermore, by aggregating the results of each monitoring indicator as its final data importance, and integrating them according to the correspondence between "monitoring indicator - data importance", a complete data importance set can be constructed to clearly present the value priority of different monitoring indicators in the production scenario of the preset time zone.

[0070] Furthermore, based on the obtained data importance set and real-time network status, the hash calculation frequency and on-chain evidence storage frequency are set to form an adapted data integrity verification strategy.

[0071] In the method provided in this application embodiment, "setting the hash calculation frequency and the on-chain evidence storage frequency based on the data importance set and real-time network status as an adapted data integrity verification strategy" includes:

[0072] Perform production load volatility analysis on the predicted operating load sequence to obtain the production load volatility coefficient within a preset time zone, wherein the production load volatility coefficient is the load standard deviation and mean in the predicted operating load sequence;

[0073] Based on the production load fluctuation coefficient, the correlation analysis of the fluctuation of the monitoring indicators is performed, and the correlation coefficient of the fluctuation of the indicators is output.

[0074] By summing 1 with the fluctuation correlation coefficients of the aforementioned indicators, several data importance compensation coefficients are obtained;

[0075] Based on the aforementioned data importance compensation coefficients, several data importances are mapped and compensated to obtain several corrected data importances;

[0076] Based on the importance of the aforementioned correction data and the real-time network status, the hash calculation frequency and the on-chain evidence storage frequency are set as an adaptation data integrity verification strategy.

[0077] In this embodiment of the application, in order to ensure that the verification frequency can match the real-time network carrying capacity and avoid network congestion or resource waste, it is necessary to optimize the data importance in combination with the characteristics of production load fluctuations and dynamically set the verification frequency in conjunction with the network status, so as to form a data integrity verification strategy that is more in line with the actual industrial operation scenario and ensure the accuracy and efficiency of data verification.

[0078] First, a production load volatility analysis is performed on the obtained predicted operating load sequence to obtain the production load volatility coefficient within the preset time zone.

[0079] Specifically, in the analysis process, firstly, the load values ​​at all time points in the predicted operating load sequence are statistically analyzed, and the mean of these load values ​​is calculated to reflect the average operating load level of the equipment within the preset time zone; then, the standard deviation of the load values ​​is calculated to reflect the degree to which the load values ​​deviate from the mean; finally, the standard deviation is divided by the mean to obtain the production load fluctuation coefficient. The larger the production load fluctuation coefficient, the more drastic the load change of the equipment within the preset time zone.

[0080] Furthermore, based on the calculated production load fluctuation coefficient, a correlation analysis of the fluctuation of several monitoring indicators is performed to output the corresponding correlation coefficients of the fluctuation of several indicators.

[0081] Specifically, correlation analysis needs to be combined with the equipment operating mechanism to determine the degree of correlation between the numerical changes of different monitoring indicators and load fluctuations. For example, for injection molding equipment, the "barrel temperature" monitoring indicator will change significantly with the increase of load, and its correlation with load fluctuations is high. The correlation coefficient of the indicator fluctuation can be set to 0.7. On the other hand, the "equipment indicator voltage" monitoring indicator is less affected by load fluctuations and has a low degree of correlation. The correlation coefficient of the indicator fluctuation can be set to 0.1.

[0082] The larger the correlation coefficient of the indicator fluctuation, the more significantly the monitoring indicator is affected by load fluctuations, and the corresponding data importance should also be increased to ensure that key data can be verified during load fluctuations.

[0083] Furthermore, the sum of 1 and several indicator fluctuation correlation coefficients is obtained to obtain several data importance compensation coefficients. For example, if the indicator fluctuation correlation coefficient of a certain monitoring indicator is 0.6, its data importance compensation coefficient is 1 + 0.6 = 1.6; if the indicator fluctuation correlation coefficient of another monitoring indicator is 0.2, its data importance compensation coefficient is 1 + 0.2 = 1.2.

[0084] By using the above summation method, we can retain the basic value of the original data's importance and make differentiated compensation based on the correlation between monitoring indicators and load fluctuations, so that the compensated importance is more in line with the dynamic operation scenario of the equipment.

[0085] Furthermore, several data importances are mapped and compensated based on several data importance compensation coefficients. The compensation method is to directly multiply the data importance compensation coefficients with the corresponding data importances to obtain several corrected data importances.

[0086] For example, if the original data importance of a certain monitoring indicator is 0.8 and the corresponding data importance compensation coefficient is 1.5, the corrected data importance is 0.8 × 1.5 = 1.2; if the original data importance is 0.5 and the data importance compensation coefficient is 1.1, the corrected data importance is 0.5 × 1.1 = 0.55.

[0087] Finally, based on the importance of several corrected data points and the real-time network status, the hash calculation frequency and the on-chain evidence storage frequency are set to form an adaptive data integrity verification strategy to match the differences in data value and network resource conditions in the scenario of predictive maintenance of industrial equipment.

[0088] The method provided in this application embodiment, which "sets the hash calculation frequency and the on-chain evidence storage frequency based on the importance of the plurality of correction data and the real-time network status as an adaptation data integrity verification strategy", includes:

[0089] Real-time monitoring of network communication performance parameters from the edge computing node to the trusted data space, wherein the performance parameters include network latency and bandwidth utilization;

[0090] Based on the matching of network latency and bandwidth utilization, the preset standard hash calculation frequency and preset standard on-chain evidence storage frequency under the current conditions are obtained;

[0091] Calculate the ratio of the importance of the several corrected data points to the average importance of several historical data points during the production of similar products to obtain several frequency optimization coefficients;

[0092] Based on the aforementioned frequency optimization coefficients, the preset standard hash calculation frequency and the preset standard on-chain evidence storage frequency are optimized respectively to obtain the adapted hash calculation frequency and the adapted on-chain evidence storage frequency as an adapted data integrity verification scheme. An adapted data integrity verification strategy is generated based on the aforementioned adapted data integrity verification schemes.

[0093] In this embodiment of the application, in order to ensure that the hash calculation frequency and the on-chain evidence storage frequency can adapt to the real-time network status and match the corrected differences in data importance, it is necessary to combine network performance monitoring and historical data reference to optimize the verification frequency step by step, so as to form a data integrity verification strategy that takes into account both data credibility and processing efficiency.

[0094] Specifically, the network performance monitoring module, deployed in the communication link between edge computing nodes and trusted data space, firstly collects and analyzes network communication performance parameters in real time during data transmission, focusing on network latency and bandwidth utilization.

[0095] Among them, network latency monitoring needs to capture the time difference between the data being transmitted from the edge node and the data being received in the trusted data space, for example, by periodically sending test data packets and recording the round-trip time; bandwidth utilization monitoring needs to calculate the ratio of the actual amount of data transmitted by the network per unit time to the network's maximum transmission capacity, for example, by collecting the current transmission rate every 10 seconds and comparing it with the network's rated bandwidth to obtain the bandwidth utilization rate.

[0096] Furthermore, based on network latency and bandwidth utilization, the preset standard hash calculation frequency and preset standard on-chain evidence storage frequency under the current conditions are matched and obtained.

[0097] The preset standard hash calculation frequency and preset standard on-chain evidence storage frequency are determined based on a mapping relationship built from extensive testing in industrial network scenarios. For example, when network latency is below 50ms and bandwidth utilization is below 60% (good network conditions), the standard hash calculation frequency can be set to once every 12 seconds, and the standard on-chain evidence storage frequency can be set to once every 25 seconds. When network latency is between 100-200ms and bandwidth utilization is between 80%-90% (strained network conditions), the standard hash calculation frequency is adjusted to once every 30 seconds, and the standard on-chain evidence storage frequency is adjusted to once every 60 seconds. This matching method ensures that the standard frequency is compatible with the network carrying capacity, avoiding network congestion due to excessively high frequencies or affecting the timeliness of data verification due to excessively low frequencies.

[0098] Furthermore, the ratios of the importance of several corrected data points to the average importance of several historical data points of the corresponding monitoring indicators during the production of similar products are calculated to obtain several frequency optimization coefficients.

[0099] Among them, the historical data importance average during the production of similar products is the benchmark value calculated by extracting the historical corrected data importance of the same monitoring indicators of similar equipment from the factory's past production cycles of the same products, and then averaging them.

[0100] For example, if the current corrected data importance of a certain monitoring indicator is 1.3, and the historical average importance of the same indicator during the production of similar products is 0.8, then the frequency optimization coefficient is 1.3 / 0.8=1.625; if the current corrected data importance is 0.6, and the historical average importance is 0.9, then the frequency optimization coefficient is 0.6 / 0.9≈0.667.

[0101] If the frequency optimization coefficient is greater than 1, it means that the importance of the current calibration data is higher than the historical average level, and the corresponding verification frequency needs to be increased to strengthen the protection; if the frequency optimization coefficient is less than 1, it means that the current calibration data is lower than the historical average level, and the frequency can be appropriately reduced to save resources.

[0102] Finally, based on the obtained frequency optimization coefficients, the preset standard hash calculation frequency and the preset standard on-chain evidence storage frequency are optimized respectively, thereby obtaining the adapted hash calculation frequency and the adapted on-chain evidence storage frequency, which are then integrated as an adapted data integrity verification scheme, and an adapted data integrity verification strategy is generated.

[0103] Specifically, the optimization method involves dividing the preset standard hash calculation frequency and the on-chain evidence storage frequency by the frequency optimization coefficient. For example, if the standard hash calculation frequency is once every 12 seconds and the frequency optimization coefficient for a certain monitoring indicator is 1.625, then the adapted hash calculation frequency is approximately once every 7 seconds (12 / 1.625). If the standard on-chain evidence storage frequency is once every 25 seconds and the frequency optimization coefficient is 1.625, then the adapted on-chain evidence storage frequency is approximately once every 15 seconds (25 / 1.625).

[0104] Finally, through the above optimization method, the adaptation frequencies of all the optimized monitoring indicators are integrated into a single adaptation data integrity verification scheme according to the correspondence of "monitoring indicator - adaptation hash calculation frequency - adaptation on-chain evidence storage frequency". Then, all adaptation data integrity verification schemes are summarized to form an adaptation data integrity verification strategy covering all monitoring indicators of the device, so as to ensure that the verification frequency of each indicator can match the network status and data value at the same time.

[0105] S120: On the edge computing node local to the factory, the real-time operating status data stream of the industrial equipment is processed according to the adapted data integrity verification strategy, a data integrity verification certificate is generated, and submitted to the distributed ledger in the trusted data space for storage.

[0106] In this embodiment of the application, in order to ensure that the real-time operating status data stream of industrial equipment is not tampered with or lost, and that its integrity can be verified later, it is necessary to rely on the local edge computing node of the factory to process the data stream according to the adapted data integrity verification strategy and generate a trusted certificate, and then submit it to the distributed ledger for evidence storage, so as to ensure that subsequent equipment maintenance analysis can be carried out based on real and complete data.

[0107] Specifically, the real-time operating status data stream of industrial equipment is first filtered, sampled, and grouped according to several adapted data integrity verification schemes. During filtering, data crucial for assessing equipment health is retained while redundant information is removed. Sampling intervals are determined based on the adapted frequency to ensure the data reflects changes in equipment operation. Grouping integrates similar monitoring data within the same time period, generating several data packets to be processed.

[0108] Furthermore, a cryptographic hash algorithm is used to calculate the hash value for each data packet to be processed. The hash value of each data packet is unique, and if the data is modified, the hash value will change accordingly, thus identifying the original state of the data packet.

[0109] Furthermore, all hash values ​​within the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated. This root hash value is the data integrity verification credential for this time window, which can comprehensively represent the integrity of all data packets within this time window.

[0110] Finally, the data integrity verification certificate, time window information, device identifier, and edge node digital signature are packaged into a notarized transaction and broadcast to the distributed ledger in the trusted data space. After verification by the ledger network consensus, the transaction is recorded in a new block, completing the notarization process and ensuring that the verification certificate is securely stored and cannot be tampered with.

[0111] This step, by processing data locally at the edge node and combining it with distributed ledger notarization, reduces the pressure of remote data transmission and ensures the credibility of verification credentials, providing a core basis for data integrity verification during subsequent equipment maintenance.

[0112] Step S120 in the method provided in this application embodiment includes:

[0113] According to the aforementioned several adapted data integrity verification schemes, the real-time running status data stream is filtered, sampled, and grouped to generate several data packets to be processed;

[0114] A cryptographic hash algorithm is used to calculate several hash values ​​for each of the several data packets to be processed.

[0115] Several hash values ​​belonging to the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated as the data integrity verification credential for the time window.

[0116] The data integrity verification certificate, time window information, device identifier, and edge node digital signature are packaged into a single notarized transaction, broadcast to the distributed ledger in the trusted data space, and recorded in a new block after network consensus, thus completing the notarization.

[0117] In this embodiment of the application, in order to ensure that the real-time operating status data stream of industrial equipment has verifiable integrity after processing, the data stream needs to be processed and a trusted verification certificate needs to be generated at the local edge computing node of the factory according to the adaptation scheme. Then, the certificate is stored through a distributed ledger to ensure that the data integrity can be quickly and accurately verified during subsequent equipment maintenance, and to provide reliable data support for predictive maintenance.

[0118] Specifically, the real-time running status data stream is first filtered, sampled, and grouped according to several adapted data integrity verification schemes to generate several data packets to be processed.

[0119] Specifically, data screening should be based on the data integrity verification scheme for each monitoring indicator, retaining core data directly related to the diagnosis of equipment health status, such as spindle speed and temperature of machine tools, and eliminating redundant information such as environmental noise and repeated data collection.

[0120] In addition, the sampling interval needs to be determined in conjunction with the appropriate hash calculation frequency. For example, if the appropriate hash calculation frequency is once every 15 seconds, then the real-time data stream is sampled at fixed intervals of 15 seconds to ensure that the sampled data can reflect the dynamic changes in the device's operating status.

[0121] In addition, grouping requires grouping the sampling data of the same monitoring indicator within the same time segment. For example, four sampling data of a certain temperature sensor within one minute are integrated into a data package, so that each data package can fully present the operating trend of a specific indicator in a short period of time, and finally form a data package to be processed with a unified structure and focused content.

[0122] Furthermore, after generating the data packets to be processed, the existing cryptographic hash algorithm is used to calculate several data packets to be processed to generate several hash values.

[0123] Among them, the cryptographic hash algorithm needs to meet the security and computational efficiency requirements in industrial scenarios. Its core characteristics are that "a small change in the input will lead to a significant difference in the output" and "it is impossible to deduce the input from the output". That is, if a data packet is tampered with during storage or transmission, even if only one bit of data is changed, the corresponding hash value will be completely different. This achieves a unique identifier of the original state of the data packet and provides a basis for subsequent integrity verification.

[0124] Furthermore, several hash values ​​belonging to the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated as the data integrity verification credential for that time window.

[0125] Specifically, the division of the time window must be consistent with the frequency of on-chain evidence storage. For example, if the on-chain frequency is once every minute, then one minute is used as a time window. When constructing the Merkle tree, all hash values ​​within the time window must first be used as leaf nodes. Then, the parent node is generated by calculating the adjacent leaf nodes pairwise using the cryptographic hash algorithm. The process is iterated upwards layer by layer until a unique root hash value is obtained.

[0126] The root hash value obtained is equivalent to the digital fingerprint of all data packets within the entire time window. During subsequent verification, it is not necessary to check each data packet one by one. The root hash value is sufficient to quickly determine whether the data within the window is complete, which effectively improves the efficiency of data integrity verification.

[0127] Finally, the data integrity verification certificate (i.e., root hash value), time window information, device identifier, and edge node digital signature are packaged into a single notarization transaction, broadcast to the distributed ledger in the trusted data space, and recorded in a new block after network consensus, thus completing the notarization.

[0128] Specifically, the time window information must clearly indicate the specific time period corresponding to the certificate to ensure accurate location of the data for the corresponding time period in the future; the device identifier is used to distinguish the evidence data of different industrial equipment to avoid confusion of data from multiple devices; the edge node digital signature must be generated through the node's exclusive private key to verify the legitimacy of the initiating entity of the evidence transaction to prevent forged transactions; after receiving the transaction, the distributed ledger verifies the authenticity and legality of the transaction content through the consensus mechanism between nodes (such as proof-of-work, proof-of-stake, etc.). After the verification is passed, the transaction is permanently recorded in a new block and synchronized to the entire distributed ledger network to ensure that the verification certificate cannot be unilaterally tampered with and always maintains a trustworthy state.

[0129] Furthermore, during the evidence preservation process, it is essential to ensure that each step of the operation is strictly consistent with the data integrity verification strategy. For example, data grouping rules, hash algorithm selection, and time window division must all be consistent with the initial strategy settings to avoid subsequent verification failures due to operational deviations.

[0130] At the same time, the broadcasting and consensus process of the evidence-based transactions need to be monitored in real time. If a network anomaly causes the transaction to fail to be uploaded to the chain, a retry mechanism needs to be triggered to ensure that the verification certificate can be recorded in the distributed ledger in a timely and accurate manner, laying the foundation for the accuracy of subsequent equipment maintenance and analysis.

[0131] S130: When the equipment maintenance service provider initiates a maintenance analysis request, the original operating status data stream of the edge computing node in the preset time zone is obtained, and the integrity of the original operating status data stream is verified based on the data integrity verification certificate on the distributed ledger.

[0132] In this embodiment of the application, in order to ensure that the original operating status data stream used by the equipment maintenance service provider for analysis is true and reliable, and to avoid drawing incorrect maintenance conclusions based on invalid data, it is necessary to obtain the original operating status data stream from the edge computing node after the service provider initiates the maintenance analysis request, and to conduct integrity verification in combination with the data integrity verification certificate in the distributed ledger, so as to ensure the accuracy of subsequent equipment health diagnosis and maintenance plan formulation.

[0133] Specifically, when a device maintenance service provider initiates a maintenance analysis request, the edge computing node, after responding to the request, will retrieve the corresponding raw operating status data stream from local storage based on the time range and device identifier specified in the request and return it to the service provider.

[0134] Furthermore, after receiving the original operating status data stream, the equipment maintenance service provider will recalculate the local hash value of each data packet according to the same hash calculation frequency and grouping rules when the original operating status data stream was generated, and then reconstruct the Merkle tree based on these hash values, finally calculating the reconstructed root hash value.

[0135] Furthermore, based on the time range and device identifier in the request, the equipment maintenance service provider queries the distributed ledger in the trusted data space to obtain the data integrity verification certificate for the memory certificate within the same time window.

[0136] Finally, the reconstructed root hash value is compared with the retrieved data integrity verification certificate: if they match, it means that the original running status data stream has not been modified during storage and transmission, and the data integrity verification is successful; if they do not match, the verification is deemed to have failed and an alarm is triggered, indicating that the equipment maintenance service provider's data may be abnormal and that the problem needs to be investigated before maintenance analysis can be carried out, in order to avoid deviations in equipment health diagnosis due to the use of tampered or damaged data, and to ensure the scientific and effective development of subsequent targeted maintenance plans.

[0137] Step S130 in the method provided in this application embodiment includes:

[0138] When the equipment maintenance service provider initiates a maintenance analysis request, after responding to the maintenance analysis request, the edge computing node retrieves and returns the corresponding original operating status data stream from local storage according to the time range and device identifier specified in the request;

[0139] After receiving the original operating status data stream, the equipment maintenance service provider recalculates the local hash value of each data packet in the original operating status data stream according to the same hash calculation frequency and grouping rules as when it was generated, and reconstructs the Merkle tree to calculate the reconstructed root hash value.

[0140] Based on the time range and device identifier, retrieve the data integrity verification certificate for the memory certificate within the same time window from the distributed ledger;

[0141] The reconstructed root hash value is compared with the data integrity verification credential. If they match, the data integrity verification is deemed successful; otherwise, the verification is deemed unsuccessful and an alarm is triggered.

[0142] In this embodiment of the application, in order to avoid the risk of industrial equipment failure caused by deviations in equipment health diagnosis and failure of maintenance plan due to data problems, it is necessary to obtain the original data, reconstruct the verification information, query the trusted credentials and complete the comparison through the edge computing node after the service provider initiates the request, so as to realize the effective verification of data integrity and provide a reliable data foundation for subsequent equipment maintenance analysis.

[0143] Specifically, when a device maintenance service provider initiates a maintenance analysis request, after responding to the request, the edge computing node will retrieve and return the corresponding raw operating status data stream from local storage based on the time range and device identifier specified in the request.

[0144] Among them, the edge computing nodes, as the core of local data storage and processing in the factory, will store the original operating status data stream in a structured manner according to the time dimension and the device dimension. For example, the data is saved in a hierarchical directory of "device number-date-time period" to ensure that after receiving a request, the complete data stream of the specified device within a specific time period can be quickly located and extracted.

[0145] In addition, the precision of the time range must be consistent with the time window used in the previous data processing. For example, if the data was processed in a 1-minute time window in the previous process, the time range in the request must also be accurate to the minute level to avoid incomplete data retrieval due to time differences. The equipment identifier must use a unique code, such as the equipment's factory serial number, to ensure accurate matching of the target industrial equipment and prevent data confusion with other equipment.

[0146] Furthermore, after the equipment maintenance service provider receives the original operating status data stream, it needs to recalculate the local hash value of each data packet according to the same hash calculation frequency and grouping rules as when the data stream was generated, and reconstruct the Merkle tree to finally calculate the reconstructed root hash value.

[0147] The hash calculation frequency must be completely consistent with the previously set adaptive hash calculation frequency. For example, if the hash value of a certain monitoring indicator was calculated once every 10 seconds in the previous stage, the received data stream must also be segmented and hashed at the same frequency. The grouping rules must also be consistent with the previous stage. For example, if the five samples of the same sensor were grouped into one data packet in the previous stage, the data must also be grouped in the same quantity and order to ensure that the coverage, data composition and generation of each data packet are completely consistent.

[0148] The local hash value calculated in the same way can truly reflect the current status of the received data. At the same time, by reconstructing the Merkle tree and calculating the corresponding reconstructed root hash value in the same way, the integrity of multiple data packets can be condensed into a unified verification identifier, laying the foundation for subsequent rapid comparison.

[0149] Furthermore, the equipment maintenance service provider will query the distributed ledger within the trusted data space to obtain the data integrity verification certificate for the memory certificate within the same time window, based on the time range and equipment identifier in the request.

[0150] Distributed ledgers, with their decentralized storage and network-wide consensus features, ensure that their recorded verification credentials cannot be unilaterally altered and can be quickly indexed using timestamps and device identifiers. Specifically, after inputting a specified device identifier and time window, the distributed ledger returns the corresponding data integrity verification credential for that period (i.e., the previously generated Merkle tree root hash value). This data integrity verification credential is the core reference standard for determining whether the currently received data is complete.

[0151] Furthermore, the obtained reconstructed root hash value is compared with the queried data integrity verification certificate to determine whether the original running state data stream has remained intact and untampered during storage and transmission.

[0152] Specifically, if the two are consistent, it means that the original operating status data stream has not been tampered with or lost during the entire process from generation and storage to transmission to the equipment maintenance service provider. The data integrity verification has passed, and the service provider can carry out equipment health status diagnosis and maintenance analysis based on the original operating status data stream.

[0153] Conversely, if the two are inconsistent, the data integrity verification is deemed to have failed. In this case, an alarm must be triggered immediately. The alarm message must clearly state "Data integrity verification failed, there may be tampering or damage." At the same time, key information such as the failure time, device identifier, and time window should be recorded to facilitate subsequent troubleshooting by technical personnel. For example, they can check whether there are security vulnerabilities in the data transmission link or whether the local storage of the edge computing node is abnormal.

[0154] Ultimately, through the above steps, a complete data integrity verification chain of "data acquisition - verification and reconstruction - voucher query - comparison and verification" was constructed, which not only ensured the credibility of the data used for maintenance analysis, but also provided a guarantee for the accuracy of predictive maintenance of industrial equipment, effectively reducing the maintenance risks caused by data problems.

[0155] S140: If the data integrity verification passes, perform equipment health status diagnosis and targeted maintenance based on the original operating status data stream.

[0156] In this embodiment of the application, in order to determine the health status of the equipment based on the actual equipment operation data, avoid diagnostic biases caused by data problems, and reduce the risk of equipment failure downtime and ineffective maintenance costs, it is necessary to carry out health diagnosis and customized maintenance based on the original operating status data stream after the data integrity verification is passed, so as to ensure stable equipment operation, extend service life and support the continuous progress of factory production.

[0157] Specifically, the first step is to extract operational data for each monitoring indicator from the raw operational status data stream that has passed integrity verification. This data covers core parameters of key subsystems of the equipment, such as the spindle temperature and speed of the machine tool, and the exhaust pressure and lubricating oil level of the compressor. During extraction, the data needs to be organized in chronological order to form operational trend curves for each indicator, so as to intuitively present the changes in the equipment's status within a preset time zone.

[0158] Furthermore, by combining the equipment's rated operating parameters, historical fault data, and industry maintenance standards, the extracted monitoring indicator data is used to diagnose its health status.

[0159] For example, by comparing the actual operating temperature of the machine tool spindle with the rated temperature range, if it continuously exceeds the rated value by more than 10%, it is determined that there is a health hazard in the spindle system; by analyzing the fluctuation trend of the compressor exhaust pressure, if the decrease exceeds 15% in a short period of time and is accompanied by abnormal motor current, a valve leakage fault can be predicted.

[0160] In addition, the diagnostic process should also be linked to similar past failure cases. If the current data characteristics match the characteristics before the historical failure, the credibility of the diagnostic results will be further improved.

[0161] Furthermore, after clarifying the health status of the equipment, a targeted maintenance plan is developed based on the diagnostic results.

[0162] Specifically, for equipment in good health but with some monitoring indicators close to the warning threshold, preventive maintenance plans are developed, such as regular cleaning and lubrication of key components; for equipment with minor health risks, restorative maintenance plans are developed, such as replacing worn parts and adjusting parameter settings; for equipment that has already malfunctioned, emergency repair plans are developed, such as shutting down the machine to replace faulty parts and coordinating backup equipment to ensure the continuity of production.

[0163] For example, if a diagnosis reveals that the barrel heating temperature of an injection molding machine fluctuates beyond the normal range, and historical data shows that this is mostly caused by the aging of the heating element, the targeted maintenance solution includes: stopping the machine after the current production batch is completed, replacing the heating element with the same model, and checking the accuracy of the temperature sensor and the condition of the barrel insulation layer to avoid related problems after maintenance.

[0164] In addition, after maintenance is completed, the temperature data of the barrel before and after maintenance should be compared to evaluate the maintenance effect, so as to ensure that the equipment returns to normal and healthy condition and provides a guarantee for subsequent stable operation.

[0165] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0166] This application proposes a joint data integrity verification method for a trusted data space. First, based on the production plan within a pre-defined time zone of the factory, the expected production tasks and expected product demands of the target industrial equipment are screened. The equipment's digital twin model is invoked to calculate the load rate of key subsystems in real time and output it chronologically, forming a predicted operating load sequence. Combining the expected product demand and the predicted operating load sequence, a data importance evaluation rule base is constructed. The importance score set of each monitoring indicator is obtained by traversing the predicted operating load sequence and then aggregated to generate a data importance set. Volatility analysis is performed on the predicted operating load sequence to obtain the production load volatility coefficient. Simultaneously, correlation analysis of indicator volatility is conducted to obtain the correlation coefficient. The data importance compensation coefficient is calculated using the correlation coefficient, and the original data importance is mapped and compensated to obtain the corrected data importance. The network latency and bandwidth utilization from edge computing nodes to the trusted data space are monitored in real time. Pre-defined standard hash calculation and standard on-chain evidence storage frequencies are matched and obtained. Frequency optimization coefficients are calculated, and the standard frequencies are optimized to generate an adapted data integrity verification scheme, which is then integrated to form an adapted data integrity verification strategy. At the local edge computing node in the factory, the real-time operating status data stream of industrial equipment is processed according to the adapted data integrity verification scheme, generating data packets to be processed. A cryptographic hash algorithm is used to calculate the hash value, a Merkle tree is constructed, and the root hash value is used as the data integrity verification credential. The data is then packaged, stored, and uploaded to the blockchain. When a service provider initiates a maintenance request, the edge computing node returns the original operating status data stream, reconstructs the Merkle tree, and compares the credential with the distributed ledger. If the verification is successful, the device health is diagnosed based on the original operating status data stream, and a targeted maintenance plan is developed.

[0167] The method provided in this application, through the technical solution of "dynamic verification strategy formulation - real-time data processing and evidence storage - data integrity verification - equipment diagnosis and maintenance", solves the problems in traditional industrial data verification, such as the inability of fixed strategies to adapt to differences in data value and changes in network status, insufficient credibility of data evidence storage, and difficulty in ensuring the authenticity of maintenance analysis data. It improves the accuracy and reliability of predictive maintenance of industrial equipment, reduces maintenance errors and production losses caused by data problems, and provides strong support for equipment management and production stability in the industrial manufacturing field.

[0168] Example 2, as shown in the appendix Figure 2 As shown, based on the inventive concept of a joint data integrity verification method for a trusted data space provided in Embodiment 1, this application also provides a joint data integrity verification system for a trusted data space, specifically including:

[0169] The verification strategy setting module 01 is used to set the hash calculation frequency and the on-chain evidence storage frequency based on the data importance in the preset time zone and the real-time network status, as an adaptation to the data integrity verification strategy.

[0170] The voucher generation and storage module 02 is used to process the real-time operating status data stream of industrial equipment on the local edge computing node of the factory according to the adapted data integrity verification strategy, generate data integrity verification vouchers, and submit them to the distributed ledger in the trusted data space for storage.

[0171] The data integrity verification module 03 is used to obtain the original operating status data stream of the edge computing node in the preset time zone when the equipment maintenance service provider initiates a maintenance analysis request, and to perform integrity verification on the original operating status data stream based on the data integrity verification certificate on the distributed ledger.

[0172] The equipment diagnosis and maintenance module 04 is used to perform equipment health status diagnosis and targeted maintenance based on the original operating status data stream if the data integrity verification passes.

[0173] In one embodiment, the verification policy setting module 01 is further configured to:

[0174] Based on the factory's production plan within a preset time zone, the expected production tasks and expected product demands of the target industrial equipment are screened out; the equipment operation load simulation of the target industrial equipment is performed according to the expected production tasks to obtain a predicted operation load sequence; based on the expected product demand and the predicted operation load sequence, the data importance of several monitoring indicators of the target industrial equipment is evaluated, and a data importance set is output; based on the data importance set and the real-time network status, the hash calculation frequency and the on-chain evidence storage frequency are set as an adaptation data integrity verification strategy.

[0175] Furthermore, the verification strategy setting module 01 also includes:

[0176] A data importance evaluation rule base is constructed, wherein the data importance evaluation rule base defines the importance weights of several monitoring indicators under different product demand and load levels; the predicted operating load sequence is traversed, and for the load value at each time point, the data importance evaluation rule base is queried in conjunction with the expected product demand to obtain a set of importance scores for several monitoring indicators; the importance score sets are aggregated and calculated to generate several data importances for several monitoring indicators, and a data importance set is constructed.

[0177] Furthermore, the verification strategy setting module 01 also includes:

[0178] A production load volatility analysis is performed on the predicted operating load sequence to obtain the production load volatility coefficient within a preset time zone. The production load volatility coefficient is the sum of the load standard deviation and mean in the predicted operating load sequence. Based on the production load volatility coefficient, a correlation analysis of the volatility of several monitoring indicators is performed, outputting several indicator volatility correlation coefficients. Each of the several indicator volatility correlation coefficients is summed with 1 to obtain several data importance compensation coefficients. Based on these data importance compensation coefficients, several data importances are mapped and compensated to obtain several corrected data importances. Based on these corrected data importances and the real-time network status, a hash calculation frequency and an on-chain evidence storage frequency are set as an adaptation data integrity verification strategy.

[0179] Furthermore, the verification strategy setting module 01 also includes:

[0180] The network communication performance parameters from the edge computing node to the trusted data space are monitored in real time, including network latency and bandwidth utilization. Based on the network latency and bandwidth utilization, a preset standard hash calculation frequency and a preset standard on-chain evidence storage frequency under the current conditions are obtained. The ratios of the importance of several corrected data points to the average importance of several historical data points from the production of similar products are calculated to obtain several frequency optimization coefficients. Based on these frequency optimization coefficients, the preset standard hash calculation frequency and the preset standard on-chain evidence storage frequency are optimized to obtain an adapted hash calculation frequency and an adapted on-chain evidence storage frequency as an adapted data integrity verification scheme. An adapted data integrity verification strategy is generated based on these adapted data integrity verification schemes.

[0181] In one embodiment, the credential generation and storage module 02 is further configured to:

[0182] According to the aforementioned several adapted data integrity verification schemes, the real-time running status data stream is filtered, sampled, and grouped to generate several data packets to be processed. A cryptographic hash algorithm is used to calculate several hash values ​​for each of the several data packets to be processed. Several hash values ​​belonging to the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated as the data integrity verification credential for the time window. The data integrity verification credential, time window information, device identifier, and edge node digital signature are packaged into a single notarized transaction, broadcast to the distributed ledger in the trusted data space, and recorded in a new block after network consensus, thus completing the notarization.

[0183] In one embodiment, the data integrity verification module 03 is further configured to:

[0184] When a device maintenance service provider initiates a maintenance analysis request, upon responding to the request, the edge computing node retrieves and returns the corresponding original operating status data stream from its local storage based on the time range and device identifier specified in the request. After receiving the original operating status data stream, the device maintenance service provider recalculates the local hash value of each data packet in the original operating status data stream according to the same hash calculation frequency and grouping rules as when it was generated, and reconstructs the Merkle tree to obtain the reconstructed root hash value. Based on the time range and device identifier, it queries the distributed ledger to obtain a data integrity verification credential for the same time window. The reconstructed root hash value is compared with the data integrity verification credential; if they match, the data integrity verification is deemed successful; otherwise, the verification is deemed unsuccessful and an alarm is triggered.

[0185] Example 3, as shown in the appendix Figure 3 As shown, based on the inventive concept of a joint verification method for data integrity in a trusted data space provided in Embodiment 1, this application also provides a computer-readable storage medium 300, on which a first computer program 311 is stored.

[0186] When the first computer program 311 is executed by the processor 420, it implements a joint verification method for the integrity of a trusted data space as described in Embodiment 1.

[0187] Example 4, as shown in the appendix Figure 4 As shown, based on the inventive concept of a joint data integrity verification method for a trusted data space provided in Embodiment 1, this application also provides a verification terminal 400, which comprises:

[0188] Memory 410 is used to store the second computer program 411;

[0189] Processor 420 is used to read and execute the second computer program 411.

[0190] When the second computer program 411 is executed by the processor 420, it implements a joint verification method for the integrity of a trusted data space as described in Embodiment 1.

[0191] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0192] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0193] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for joint verification of data integrity in a trusted data space, characterized in that, The methods include: The hash calculation frequency and on-chain evidence storage frequency are set based on the data importance in the preset time zone and the real-time network status as an adaptation data integrity verification strategy. On the edge computing node local to the factory, the real-time operating status data stream of industrial equipment is processed according to the adapted data integrity verification strategy, a data integrity verification certificate is generated, and submitted to the distributed ledger in the trusted data space for storage. When the equipment maintenance service provider initiates a maintenance analysis request, it obtains the original operating status data stream of the edge computing node within the preset time zone, and performs integrity verification on the original operating status data stream based on the data integrity verification certificate on the distributed ledger. If the data integrity verification passes, the device health status diagnosis and targeted maintenance are performed based on the original operating status data stream.

2. The method for joint verification of data integrity in a trusted data space according to claim 1, characterized in that, The hash calculation frequency and on-chain evidence storage frequency are set based on the data importance within a preset time zone and the real-time network status, as an adaptation to the data integrity verification strategy, including: Based on the factory's production plan within the preset time zone, the expected production tasks and expected product demands of the target industrial equipment are obtained by screening. Based on the expected production tasks, the target industrial equipment is simulated for equipment operation load to obtain the predicted operation load sequence; Based on the expected product demand and predicted operating load sequence, the data importance of several monitoring indicators of the target industrial equipment is evaluated, and a data importance set is output. The hash calculation frequency and on-chain evidence storage frequency are set based on the data importance set and real-time network status as an adaptation data integrity verification strategy.

3. The method for joint verification of data integrity in a trusted data space according to claim 2, characterized in that, Based on the expected product demand and predicted operating load sequence, the data importance of several monitoring indicators for the target industrial equipment is evaluated, and a data importance set is output, including: A data importance evaluation rule base is constructed, wherein the data importance evaluation rule base defines the importance weights of several monitoring indicators under different product demand and load levels; Traverse the predicted operating load sequence, and for the load value at each time point, query the data importance evaluation rule base in conjunction with the expected product demand to obtain a set of importance scores for several monitoring indicators; The importance score set is aggregated and calculated to generate several data importance scores for several monitoring indicators, and a data importance set is constructed.

4. The method for joint verification of data integrity in a trusted data space according to claim 2, characterized in that, Based on the data importance set and real-time network status, the hash calculation frequency and on-chain evidence storage frequency are set as an adaptation to the data integrity verification strategy, including: Perform production load volatility analysis on the predicted operating load sequence to obtain the production load volatility coefficient within a preset time zone, wherein the production load volatility coefficient is the load standard deviation and mean in the predicted operating load sequence; Based on the production load fluctuation coefficient, the correlation analysis of the fluctuation of the monitoring indicators is performed, and the correlation coefficient of the fluctuation of the indicators is output. By summing 1 with the fluctuation correlation coefficients of the aforementioned indicators, several data importance compensation coefficients are obtained; Based on the aforementioned data importance compensation coefficients, several data importances are mapped and compensated to obtain several corrected data importances; Based on the importance of the aforementioned correction data and the real-time network status, the hash calculation frequency and the on-chain evidence storage frequency are set as an adaptation data integrity verification strategy.

5. The method for joint verification of data integrity in a trusted data space according to claim 4, characterized in that, Based on the importance of the aforementioned correction data and the real-time network status, the hash calculation frequency and the on-chain evidence storage frequency are set as an adaptation data integrity verification strategy, including: Real-time monitoring of network communication performance parameters from the edge computing node to the trusted data space, wherein the performance parameters include network latency and bandwidth utilization; Based on the matching of network latency and bandwidth utilization, the preset standard hash calculation frequency and preset standard on-chain evidence storage frequency under the current conditions are obtained; Calculate the ratio of the importance of the several corrected data points to the average importance of several historical data points during the production of similar products to obtain several frequency optimization coefficients; Based on the aforementioned frequency optimization coefficients, the preset standard hash calculation frequency and the preset standard on-chain evidence storage frequency are optimized respectively to obtain the adapted hash calculation frequency and the adapted on-chain evidence storage frequency as an adapted data integrity verification scheme. An adapted data integrity verification strategy is generated based on the aforementioned adapted data integrity verification schemes.

6. The method for joint verification of data integrity in a trusted data space according to claim 5, characterized in that, The real-time operating status data stream of industrial equipment is processed according to the adapted data integrity verification strategy to generate a data integrity verification certificate, which is then submitted to a distributed ledger in a trusted data space for storage, including: According to the aforementioned several adapted data integrity verification schemes, the real-time running status data stream is filtered, sampled, and grouped to generate several data packets to be processed; A cryptographic hash algorithm is used to calculate several hash values ​​for each of the several data packets to be processed. Several hash values ​​belonging to the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated as the data integrity verification credential for the time window. The data integrity verification certificate, time window information, device identifier, and edge node digital signature are packaged into a single notarized transaction, broadcast to the distributed ledger in the trusted data space, and recorded in a new block after network consensus, thus completing the notarization.

7. The method for joint verification of data integrity in a trusted data space according to claim 1, characterized in that, When a device maintenance service provider initiates a maintenance analysis request, the system acquires the original operational status data stream of the edge computing node within the preset time zone, and performs integrity verification on the original operational status data stream based on the data integrity verification credentials on the distributed ledger, including: When the equipment maintenance service provider initiates a maintenance analysis request, after responding to the maintenance analysis request, the edge computing node retrieves and returns the corresponding original operating status data stream from local storage according to the time range and device identifier specified in the request; After receiving the original operating status data stream, the equipment maintenance service provider recalculates the local hash value of each data packet in the original operating status data stream according to the same hash calculation frequency and grouping rules as when it was generated, and reconstructs the Merkle tree to calculate the reconstructed root hash value. Based on the time range and device identifier, retrieve the data integrity verification certificate for the memory certificate within the same time window from the distributed ledger; The reconstructed root hash value is compared with the data integrity verification credential. If they match, the data integrity verification is deemed successful; otherwise, the verification is deemed unsuccessful and an alarm is triggered.

8. A joint data integrity verification system for a trusted data space, characterized in that, The system is used to execute the data integrity joint verification method for a trusted data space as described in any one of claims 1-7, the system comprising: The verification strategy setting module is used to set the hash calculation frequency and the on-chain evidence storage frequency based on the data importance in the preset time zone and the real-time network status, as an adaptation to the data integrity verification strategy. The certificate generation and storage module is used to process the real-time operating status data stream of industrial equipment on the local edge computing node of the factory according to the adapted data integrity verification strategy, generate data integrity verification certificates, and submit them to the distributed ledger in the trusted data space for storage. The data integrity verification module is used to obtain the original operating status data stream of the edge computing node in the preset time zone when the equipment maintenance service provider initiates a maintenance analysis request, and to perform integrity verification on the original operating status data stream based on the data integrity verification certificate on the distributed ledger. The equipment diagnosis and maintenance module is used to perform equipment health status diagnosis and targeted maintenance based on the original operating status data stream if the data integrity verification passes.

9. A computer-readable storage medium, characterized in that, The storage medium stores a first computer program, which, when executed by a processor, implements a data integrity joint verification method for a trusted data space as described in any one of claims 1-7.

10. A verification terminal, characterized in that, The verification terminal includes a processor and a memory: Memory, used to store a second computer program; A processor is configured to read and execute the second computer program, thereby implementing the data integrity joint verification method for a trusted data space as described in any one of claims 1-7.

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