A data integrity joint verification method and system of a trusted data space

Abstract

The application relates to a data integrity joint verification method and system of a trusted data space, and relates to the technical field of industrial data verification, which comprises the following steps: based on the data importance, hash calculation frequency and upper chain storage frequency in a preset time zone, a data integrity verification strategy is adapted; real-time running state data flow of an industrial device is processed, data integrity verification credentials are generated, and the data integrity verification credentials are stored in a trusted data space distributed ledger; when a maintenance analysis request is initiated, the original running state data flow obtained is verified for integrity based on the data integrity verification credentials on the distributed ledger; if the verification is passed, device health state diagnosis and targeted maintenance are performed according to the original running state data flow. The application solves the problem that traditional data integrity verification methods mostly adopt fixed verification strategies, are not adapted to data importance and network states, and cannot guarantee data credibility while meeting the efficiency and real-time requirement of predictive maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial data verification, in particular to a data integrity joint verification method and system of a trusted data space. BACKGROUND

[0002] With the increasing dependence of the industrial manufacturing field on equipment predictive maintenance, the completeness and verification efficiency of real-time running state data of industrial equipment have become key technical requirements to support equipment health diagnosis and reduce fault risk.

[0003] At present, traditional data integrity verification methods mostly use fixed verification strategies, which do not dynamically adjust according to the importance of data and real-time network state in industrial scenarios. This not only makes it difficult to adapt to the credibility requirements of different monitoring indicators, but also easily causes redundant storage, calculation overhead and network delay due to indiscriminate verification. Therefore, it cannot meet the requirements of data processing efficiency and real-time performance for predictive maintenance while ensuring data credibility, increasing the risk of equipment maintenance misjudgment and fault warning lag. SUMMARY

[0004] The present application provides a data integrity joint verification method and system of a trusted data space, which improves the current situation that traditional data integrity verification methods mostly use fixed verification strategies and do not adapt to data importance and network state, which cannot meet the efficiency and real-time requirements of industrial equipment predictive maintenance while ensuring data credibility.

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

[0006] In a first aspect, the embodiments of the present application provide a data integrity joint verification method of a trusted data space, which comprises:

[0007] Setting the hash calculation frequency and the on-chain storage frequency based on the data importance and the real-time network state in a preset time zone as an adaptive data integrity verification strategy;

[0008] On the edge computing node in the factory, processing the real-time running state data stream of the industrial equipment according to the adaptive data integrity verification strategy, generating a data integrity verification credential, and submitting it to the distributed ledger in the trusted data space for storage;

[0009] When the equipment maintenance service provider initiates a maintenance analysis request, obtaining the original running state data stream of the edge computing node in the preset time zone, and verifying the integrity of the original running state data stream based on the data integrity verification credential on the distributed ledger;

[0010] If the data integrity verification is passed, the equipment health state diagnosis and targeted maintenance are performed according to the original running state data stream.

[0011] In a second aspect, the embodiments of the present application provide a data integrity joint verification system of a trusted data space, the system comprising:

[0012] A verification policy setting module is configured to set a hash calculation frequency and an on-chain storage frequency based on data importance in a preset time zone and real-time network status, as an adaptive data integrity verification policy.

[0013] A certificate generation and storage module is configured to process real-time running state data streams of industrial equipment according to the adaptive data integrity verification policy on an edge computing node locally in a factory, generate data integrity verification certificates, and submit the data integrity verification certificates to a distributed ledger in a trusted data space for storage.

[0014] A data integrity verification module is configured to obtain original running state data streams of the edge computing node in the preset time zone when a device maintenance service provider initiates a maintenance analysis request, and perform integrity verification on the original running state data streams based on data integrity verification certificates on the distributed ledger.

[0015] A device diagnosis and maintenance module is configured to perform device health state diagnosis and targeted maintenance according to the original running state data streams if the data integrity verification is passed.

[0016] In a third aspect, the embodiments of the present application provide a computer readable storage medium, the storage medium storing a first computer program, and the first computer program is executed by a processor to implement the data integrity joint verification method of the trusted data space according to the first aspect.

[0017] In a fourth aspect, the embodiments of the present application provide a verification terminal, the verification terminal comprising:

[0018] A memory is configured to store a second computer program.

[0019] A processor is configured to read and execute the second computer program, thereby implementing the data integrity joint verification method of the trusted data space according to the first aspect.

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

[0021] The application provides a data integrity joint verification method and system of a trusted data space. Through the cooperative operation of adaptive verification strategy setting, real-time data stream processing and certificate storage, data integrity verification, device health diagnosis and maintenance, the precise guarantee and efficient application of data integrity in the predictive maintenance process of industrial equipment are realized. First, based on the production plan in the preset time zone of the factory, the expected production task and product demand of the target industrial equipment are screened, the predicted running load sequence is obtained through device running load simulation, the importance of the device monitoring indicators is analyzed combined with the data importance evaluation rule library, and the data importance set is output; combined with the production load volatility and real-time network communication performance parameters, the hash calculation frequency and the chain storage frequency are optimized to set the adaptive data integrity verification strategy; then, on the factory local edge computing node, the real-time running state data stream of the industrial equipment is screened, sampled and grouped, the hash value is generated by using the encryption hash algorithm, and the Merkle tree is constructed, the root hash value is taken as the data integrity verification certificate, and the time window information, device identifier and the like are packaged into a storage transaction, and broadcast to the distributed ledger of the trusted data space to complete the storage; when the device maintenance service provider initiates a maintenance analysis request, the original running state data stream is obtained from the edge computing node, the hash value is recalculated and the Merkle tree is reconstructed, and the verification certificate queried in the distributed ledger is compared to complete the integrity verification; finally, if the verification is passed, the device health state diagnosis is carried out based on the original data stream, and the targeted maintenance scheme is developed and executed.

[0022] The technical scheme of the application solves the problems of traditional industrial data verification, such as fixed strategy that cannot match data importance difference and network state change, insufficient data storage credibility, and difficult to guarantee the authenticity of maintenance analysis data, realizes the whole-process 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. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 A flowchart of a data integrity joint verification method of a trusted data space provided by an embodiment of the application;

[0025] Figure 2 A structure diagram of a data integrity joint verification system of a trusted data space provided by an embodiment of the application;

[0026] Figure 3A structural schematic diagram of the computer readable storage medium provided by the embodiment of the present application is shown in the figure.

[0027] Figure 4 A structural schematic diagram of the terminal for checking provided by the embodiment of the present application is shown in the figure.

[0028] In the figure, the components represented by each number are described as follows:

[0029] The checking policy setting module 01, the certificate generation and storage module 02, the data integrity verification module 03, the device diagnosis and maintenance module 04, the computer readable storage medium 300, the first computer program 311, the terminal for checking 400, the memory 410, the processor 420, and the second computer program 411. DETAILED DESCRIPTION

[0030] The present application provides a data integrity joint checking method and system of a trusted data space, which is used to solve the technical problem that the conventional data integrity checking method in the prior art adopts a fixed checking policy and is not adapted to the data importance and real-time network state in the industrial scene, resulting in difficulty in balancing data credibility and processing efficiency and inability to meet the demand of industrial equipment predictive maintenance for data integrity guarantee and real-time and low overhead.

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0032] In the description of the present application, the terms “first” and “second” are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with “first” and “second” can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of “multiple” is two or more, unless otherwise specifically limited.

[0033] In the description of the present application, the term "for example" is used to mean "serving as an instance, example or illustration". Any embodiment described as "for example" in the present application is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of the application. It will be apparent to one skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated upon in order to avoid obscuring the description of the present application. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0034] Embodiment one, as shown in the accompanying drawings Figure 1 The present application provides a data integrity joint verification method of a trusted data space, which comprises the following steps:

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

[0036] In the embodiment of the present application, in the scenario of predictive maintenance of industrial equipment, in order to avoid resource waste or insufficient verification caused by traditional fixed verification strategy, ensure that data integrity verification can match the actual value of industrial data, and adapt to real-time network bearing capacity, dynamic verification strategy needs to be developed in combination with the production situation in the preset time zone and the network state, so as to improve the pertinence and efficiency of data verification.

[0037] Specifically, first, based on the production plan of the factory in the preset time zone, the expected production task and the expected product demand of the target industrial equipment to be maintained and analyzed are screened.

[0038] Further, according to the expected production task, the equipment running load of the target industrial equipment is simulated, the digital twin model of the target industrial equipment is called, and the expected production task is input into the digital twin model to carry out running simulation. In the simulation process, the load rate of the key subsystem of the target industrial equipment is calculated in real time, and the load rate of the key subsystem is output in time sequence to form a predicted running load sequence.

[0039] Further, according to the expected product demand and the predicted running load sequence, the data importance of a plurality of monitoring indexes of the target industrial equipment is evaluated, the influence of each monitoring index on the equipment running state evaluation under different product demands is comprehensively considered, and the key degree of the index when the load changes is comprehensively considered, and finally a data importance set is output.

[0040] Further, combined with the real-time network state, the network communication between the edge computing node and the trusted data space is analyzed, and the frequency of hash calculation and the interval of chain storage are dynamically adjusted. The set hash calculation frequency and chain storage frequency are integrated into the adaptive data integrity verification strategy.

[0041] This step considers the whole aspect from the production plan to the network state, so that the data integrity verification strategy can focus on the effective verification of key data and avoid the invalid occupation of network resources, providing adaptive execution basis for the subsequent processing of real-time running state data flow, verification certificate generation and storage.

[0042] The step S110 in the method provided by the embodiment of the application comprises:

[0043] Based on the production plan of the factory in the preset time zone, the expected production task and the expected product demand of the target industrial equipment are screened out;

[0044] According to the expected production task, the equipment running load simulation of the target industrial equipment is performed to obtain a predicted running load sequence;

[0045] According to the expected product demand and the predicted running load sequence, the data importance of a plurality of monitoring indexes of the target industrial equipment is evaluated, and a data importance set is output;

[0046] Based on the data importance set and the real-time network state, the hash calculation frequency and the chain storage frequency are set as an adaptive data integrity verification strategy.

[0047] In the embodiment of the application, in order to avoid the problem that the traditional verification strategy cannot adapt to the data value difference and network state change in the predictive maintenance scene of the industrial equipment, the adaptive data integrity verification strategy is gradually derived by combining the factory production plan, the equipment running feature, the data importance and the real-time network state, so as to improve the accuracy of data verification and the applicability of the industrial scene.

[0048] Specifically, first, based on the production plan of the factory in the preset time zone, the expected production task and the expected product demand of the target industrial equipment are screened out.

[0049] The target industrial equipment is the device to be maintained and analyzed, and the screening process needs to accurately match the production arrangement related to the device in the production plan, so as to ensure that the expected production task obtained can reflect the actual running demand of the device in the preset time zone, and the expected product demand can reflect the quality and specification requirements of the device production output, laying a foundation for subsequent equipment running load analysis.

[0050] Further, according to the expected production task, the equipment running load simulation of the target industrial equipment is performed to obtain a predicted running load sequence.

[0051] Specifically, the digital twin model of the target industrial equipment is called first during simulation, which can accurately replicate the physical structure, operation mechanism and subsystem correlation of the equipment, and then the expected production task selected is input into the digital twin model to carry out operation simulation.

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

[0053] Finally, through the simulation of the digital twin model, the load rates of the key subsystems are obtained in real time, and then the load rates of the key subsystems are output in time sequence, and finally the predicted operation load sequence is formed.

[0054] Further, after obtaining the expected product demand and the predicted operation load sequence, the data importance of a plurality of monitoring indexes of the target industrial equipment needs to be evaluated to output a data importance set.

[0055] The method provided in the embodiments of the present application includes:

[0056] A data importance evaluation rule library is constructed, wherein the data importance evaluation rule library defines the importance weights of a plurality of monitoring indexes under different product demands and load levels;

[0057] The predicted operation load sequence is traversed, and for the load value of each time point, the data importance evaluation rule library is queried in combination with the expected product demand to obtain an importance score set of a plurality of monitoring indexes;

[0058] The importance score set is aggregated to generate a plurality of data importance of a plurality of monitoring indexes, and a data importance set is constructed.

[0059] In the embodiments of the present application, in order to avoid the problem that the key data is not paid attention to and the non-key data occupies too much verification resources due to the use of a unified evaluation standard for all monitoring indexes of the industrial equipment, the data importance of the monitoring indexes is evaluated in combination with the expected product demand and the equipment operation load characteristics in steps to generate a data importance set that can reflect the value difference of the indexes, thereby providing a basis for the setting of the subsequent hash calculation frequency and the frequency of on-chain storage.

[0060] Specifically, first, a data importance evaluation rule library is constructed. Among them, the monitoring indicators need to be set based on the type of industrial equipment, for example, for a numerical control machine tool, the monitoring indicators can include spindle speed, cutting force, guide rail temperature, etc.; for an industrial robot, the monitoring indicators can include joint torque, motion accuracy, motor temperature, etc.

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

[0062] For example, when producing high-precision parts (high product demand) and the equipment is in a high-load running state, the monitoring indicators directly related to machining precision need to be set to a higher weight, and the monitoring indicators with low correlation to the core function of the equipment have a relatively low weight, so as to ensure that the weight distribution can truly reflect the influence degree of the indicators on the equipment health status and production quality in different scenarios.

[0063] Further, after the construction of the data importance evaluation rule library is completed, the obtained predicted running load sequence is traversed, and for the load value of each time point, the importance scores of a plurality of monitoring indicators are obtained by querying the rule library in combination with the expected product demand.

[0064] Specifically, the traversal process needs to analyze each time point in the predicted running load sequence one by one, and the load value of each time point represents the running load level of the equipment at that time, in combination with the expected product demand at that time, the key demand of the equipment running at that time can be accurately located.

[0065] For example, the load value of the equipment at a certain time point is 90% of the rated load (high load), and the expected product is a precision instrument accessory (high product demand), at this time, the data importance evaluation rule library is queried, and the monitoring indicators related to the accuracy of the equipment and the load of the core components will obtain higher importance scores, and the monitoring indicators related to the auxiliary functions will have relatively lower scores. Each time point will form a set of monitoring indicator importance scores corresponding to the current scenario.

[0066] Finally, the importance score sets of all time points are aggregated and calculated to generate a plurality of data importance degrees of monitoring indicators, and a data importance degree set is constructed accordingly.

[0067] Specifically, the aggregation calculation needs to comprehensively consider the load characteristics and product demand correlation degree of different time points, and adopts a weighted average calculation method to give higher weights to time points with high load fluctuations and critical product demands, so as to ensure that the importance of indicators in these critical scenarios accounts for a higher proportion in the final result.

[0068] Exemplarily, in the preset time zone, different stages such as device startup, high-load operation, shutdown and the like are covered, a higher weight can be given to the time point of the high-load operation stage, so that the finally calculated monitoring indicator data importance is more in line with the needs of the core operation scene of the device.

[0069] Further, the aggregation result of each monitoring indicator is taken as the final data importance thereof, and is integrated according to the correspondence of "monitoring indicator-data importance", that is, the data importance set can be constructed to clearly present the value priority of different monitoring indicators in the preset time zone production scene.

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

[0071] In the method provided by the embodiments of the present application, "setting the hash calculation frequency and the on-chain storage frequency based on the data importance set and the real-time network state as an adaptive data integrity verification strategy" includes:

[0072] Performing production load fluctuation analysis on the predicted operation load sequence to obtain a production load fluctuation coefficient in the preset time zone, wherein the production load fluctuation coefficient is a standard deviation of load in the predicted operation load sequence and a mean value;

[0073] Performing index fluctuation correlation analysis on the plurality of monitoring indicators according to the production load fluctuation coefficient, and outputting a plurality of index fluctuation correlation coefficients;

[0074] Adding and summing up the plurality of index fluctuation correlation coefficients respectively to obtain a plurality of data importance compensation coefficients;

[0075] Mapping and compensating the plurality of data importance according to the plurality of data importance compensation coefficients to obtain a plurality of corrected data importance;

[0076] Setting the hash calculation frequency and the on-chain storage frequency based on the plurality of corrected data importance and the real-time network state as an adaptive data integrity verification strategy.

[0077] In the embodiments of the present application, in order to ensure that the verification frequency can match the real-time network bearing capacity and avoid network congestion or resource waste, the data importance needs to be optimized in combination with the production load fluctuation characteristics, and the verification frequency needs to be dynamically set in linkage with the network state, so as to form an adaptive data integrity verification strategy that is more in line with the actual operation scene of the industry, and to guarantee the accuracy and efficiency of data verification.

[0078] First, the production load fluctuation analysis is performed on the obtained predicted operation load sequence to obtain a production load fluctuation coefficient in the preset time zone.

[0079] Specifically, in the analysis process, first, the load values of all time points in the predicted running load sequence are counted, and the mean of the load values is calculated to reflect the average running load level of the equipment in the preset time zone; then the standard deviation of the load values is calculated to reflect the degree of deviation of the load values 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 intense the load change of the equipment in the preset time zone.

[0080] Further, according to the calculated production load fluctuation coefficient, a number of monitoring indicators are subjected to index fluctuation correlation analysis to output a number of corresponding index fluctuation correlation coefficients.

[0081] Specifically, the correlation analysis needs to combine the equipment operation mechanism to judge the correlation degree of the numerical change of different monitoring indicators and the load fluctuation. For example, for an injection molding machine equipment, the "barrel temperature" monitoring indicator will change significantly with the increase of the load, and its correlation degree with the load fluctuation is high, and the index fluctuation correlation coefficient can be set to 0.7; while the "device indicator light voltage" monitoring indicator is less affected by the load fluctuation, and the correlation degree is low, and the index fluctuation correlation coefficient can be set to 0.1.

[0082] Among them, the larger the index fluctuation correlation coefficient, the more significant the monitoring indicator is affected by the load fluctuation, and the corresponding data importance also needs to be increased accordingly to ensure that the key data during the load fluctuation can be checked.

[0083] Further, 1 is added to a number of index fluctuation correlation coefficients to obtain a number of data importance compensation coefficients. For example, the index fluctuation correlation coefficient of a certain monitoring indicator is 0.6, and its data importance compensation coefficient is 1+0.6=1.6; the index fluctuation correlation coefficient of another monitoring indicator is 0.2, and the data importance compensation coefficient is 1+0.2=1.2.

[0084] Through the above addition and summation calculation method, not only the basic value of the original data importance can be retained, but also the correlation degree of the monitoring indicator and the load fluctuation can be compensated differently, so that the importance after compensation is more suitable for the dynamic running scene of the equipment.

[0085] Further, a number of data importances are mapped and compensated according to a number of data importance compensation coefficients. The compensation method adopts the way of directly multiplying the data importance compensation coefficient and the corresponding data importance to obtain a number of corrected data importances.

[0086] For example, if the original data importance of a certain monitoring indicator is 0.8, the corresponding data importance compensation coefficient is 1.5, and the corrected data importance is 0.8x1.5=1.2; if the original data importance is 0.5, the data importance compensation coefficient is 1.1, and the corrected data importance is 0.5x1.1=0.55.

[0087] Finally, based on the obtained several correction data importance and real-time network state set hash calculation frequency and chain storage frequency, form the adaptive data integrity check strategy to match the data value difference and network resource situation under the scene of industrial equipment predictive maintenance.

[0088] In the method provided by the embodiments of the application, "setting the hash calculation frequency and the chain storage frequency based on the several correction data importance and the real-time network state as the adaptive data integrity check strategy" comprises:

[0089] monitoring the network communication performance parameters of the edge computing node to the trusted data space in real time, wherein the performance parameters comprise network delay and bandwidth utilization;

[0090] obtaining the preset standard hash calculation frequency and the preset standard chain storage frequency under the current condition according to the network delay and the bandwidth utilization;

[0091] calculating the ratio of the several correction data importance to the average of the several historical data importance of the same product during production, to obtain several frequency optimization coefficients;

[0092] optimizing the preset standard hash calculation frequency and the preset standard chain storage frequency based on the several frequency optimization coefficients respectively, to obtain the adaptive hash calculation frequency and the adaptive chain storage frequency as the adaptive data integrity check scheme, and generating the adaptive data integrity check strategy according to the several adaptive data integrity check schemes.

[0093] In the embodiments of the application, in order to ensure that the hash calculation frequency and the chain storage frequency can adapt to the real-time network state and match the difference in data importance after correction, the verification frequency needs to be optimized in steps in combination with network performance monitoring and reference of historical data, so as to form the adaptive data integrity check strategy which takes into account data credibility and processing efficiency.

[0094] Specifically, first, through the network performance monitoring block deployed in the communication link between the edge computing node and the trusted data space, the network communication performance parameters in the data transmission process are collected and analyzed in real time, and the network delay and the bandwidth utilization are focused on.

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

[0096] Further, according to the network delay and bandwidth utilization, the preset standard hash calculation frequency and the preset standard on-chain storage frequency under the current condition are matched.

[0097] The preset standard hash calculation frequency and the preset standard on-chain storage frequency are determined based on a mapping relationship constructed by a large number of industrial network scene tests. For example, when the network delay is less than 50 ms and the bandwidth utilization is less than 60% (network status is good), the standard hash calculation frequency can be set to once every 12 seconds, and the standard on-chain storage frequency is set to once every 25 seconds; when the network delay is between 100-200 ms and the bandwidth utilization is between 80%-90% (network status is tight), the standard hash calculation frequency is adjusted to once every 30 seconds, and the standard on-chain storage frequency is adjusted to once every 60 seconds. Through the above matching method, the standard frequency can be adapted to the network bearing capacity, avoiding network congestion caused by too high frequency, or affecting the timeliness of data verification caused by too low frequency.

[0098] Further, the ratio of the importance of the correction data to the average importance of the historical data of the corresponding monitoring indicators during the production of similar products is calculated to obtain a plurality of frequency optimization coefficients.

[0099] The average importance of the historical data during the production of similar products is a reference value obtained by calculating the arithmetic mean of the historical correction data importance of the same monitoring indicators of the same equipment in the same product production cycle of the factory.

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

[0101] The frequency optimization coefficient is greater than 1, indicating that the current correction data importance is higher than the historical average level, and the corresponding verification frequency needs to be improved to strengthen the protection; the frequency optimization coefficient is less than 1, indicating that the current correction data is lower than the historical average level, and the frequency can be appropriately reduced to save resources.

[0102] Finally, based on the obtained plurality of frequency optimization coefficients, the preset standard hash calculation frequency and the preset standard on-chain storage frequency are optimized to obtain adaptive hash calculation frequency and adaptive on-chain storage frequency, and are integrated as an adaptive data integrity verification scheme, and an adaptive data integrity verification strategy is generated.

[0103] Specifically, the optimization adopts a manner of dividing the preset standard hash calculation frequency and the upper chain storage frequency by the frequency optimization coefficient, for example, the standard hash calculation frequency is 1 time per 12 seconds, the frequency optimization coefficient of a certain monitoring index is 1.625, and then the adaptive hash calculation frequency is 12 / 1.625≈7 seconds per time; the standard upper chain storage frequency is 1 time per 25 seconds, and the frequency optimization coefficient is 1.625, and then the adaptive upper chain storage frequency is 25 / 1.625≈15 seconds per time.

[0104] Finally, by the above optimization manner, the adaptive frequencies of all monitoring indexes obtained by optimization are integrated into a single adaptive data integrity verification scheme according to the corresponding relationship of “monitoring index-adaptive hash calculation frequency-adaptive upper chain storage frequency”, and all adaptive data integrity verification schemes are summarized to form an adaptive data integrity verification strategy covering all monitoring indexes of the equipment, so as to ensure that the verification frequency of each index can match the network state and data value at the same time.

[0105] S120: processing the real-time running state data stream of the industrial equipment according to the adaptive data integrity verification strategy on the edge computing node locally in the factory, generating a data integrity verification credential, and submitting the data integrity verification credential to a distributed ledger in a trusted data space for storage;

[0106] In the embodiments of the present application, in order to ensure that the real-time running state data stream of the industrial equipment is not tampered with or lost, and the integrity thereof can be verified subsequently, the factory local edge computing node needs to be relied on to process the data stream according to the adaptive data integrity verification strategy and generate a trusted credential, and then the trusted credential is submitted to the distributed ledger for storage, so as to ensure that the subsequent equipment maintenance and analysis can be carried out based on the real and complete data.

[0107] Specifically, first, according to a plurality of adaptive data integrity verification schemes, the real-time running state data stream of the industrial equipment is subjected to data screening, sampling and grouping. When screening, the data critical to the judgment of the health of the equipment needs to be retained, and redundant information needs to be excluded; sampling is determined according to the adaptive frequency interval to ensure that the data can reflect the running change of the equipment; grouping integrates the same type of monitoring data in the same time period to generate a plurality of data packets to be processed.

[0108] Further, an encryption hash algorithm is used to calculate each data packet to be processed to obtain a corresponding hash value. The hash value of each data packet is unique, and if the data is changed, the hash value will change accordingly to identify the original state of the data packet.

[0109] Further, all hash values in the same time window are constructed into a Merkle tree, and the root hash value of the Merkle tree is calculated. The root hash value is the data integrity verification credential of the time window, which can comprehensively represent the integrity of all data packets in the time window.

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

[0111] This step reduces the pressure of remote data transmission by processing data locally at the edge node and storing it in the distributed ledger, ensuring the credibility of the verification credential and providing a core basis for data integrity verification during subsequent device maintenance.

[0112] The method provided in the embodiments of the present application comprises the following steps:

[0113] According to the several adaptive data integrity verification schemes, the real-time running state data stream is filtered, sampled and grouped to generate several data packets to be processed.

[0114] The several data packets to be processed are calculated using an encryption hash algorithm to generate several hash values.

[0115] The 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 of the time window.

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

[0117] In the embodiments of the present application, in order to ensure that the real-time running state data stream of the industrial equipment has verifiable integrity after processing, the data stream is processed by the local edge computing node in the factory according to the adaptive scheme to generate a trusted verification credential, and then stored through the distributed ledger to ensure that the data integrity can be quickly and accurately verified during subsequent device maintenance, providing reliable data support for predictive maintenance.

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

[0119] Specifically, data filtering needs to retain core data directly related to device health status diagnosis according to the adaptive data integrity verification scheme of each monitoring index, such as spindle speed, temperature and other data of machine tool equipment, and eliminate redundant information such as environmental noise and repeated collection.

[0120] In addition, the sampling needs to be combined with the determination interval of the adaptive hash calculation frequency, for example, when the adaptive hash calculation frequency is 1 time per 15 seconds, that is, the real-time data stream is sampled at an interval of 15 seconds, to ensure that the sampled data can reflect the dynamic changes of the device running state.

[0121] In addition, the grouping needs to group the sampling data of the same monitoring index in the same time segment, for example, integrating the 4 sampling data of a certain temperature sensor in 1 minute into a data packet, so that each data packet can fully present the running trend of a specific index in a short period of time, and finally form a to-be-processed data packet with unified structure and focused content.

[0122] Further, after generating the to-be-processed data packet, the existing encryption hash algorithm is used to calculate a plurality of to-be-processed data packets respectively to generate a plurality of hash values.

[0123] The encryption hash algorithm needs to meet the security and calculation efficiency requirements in the industrial scene, and its core characteristics are "minor changes in input lead to significant differences in output" and "it is impossible to infer the input from the output", that is, if a data packet is tampered with during storage or transmission, even if only 1 bit of data is changed, the corresponding hash value will be completely different, thereby realizing the unique identification of the original state of the data packet and providing a basis for subsequent integrity verification.

[0124] Further, a plurality of 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 of the time window.

[0125] Specifically, the division of the time window needs to be consistent with the adaptive on-chain storage frequency, for example, when the on-chain frequency is 1 time per 1 minute, that is, 1 minute is taken as a time window; when constructing the Merkle tree, all hash values in the time window are taken as leaf nodes, and then the parent nodes are generated by calculating the adjacent leaf nodes two by two through the encryption hash algorithm, and the iteration is performed layer by layer upwards until the unique root hash value is obtained.

[0126] Among them, the finally obtained root hash value is equivalent to the digital fingerprint of all data packets in the entire time window, and subsequent verification does not need to check each data packet one by one, but only needs to quickly judge whether the data in the window is complete through the root hash value, thereby effectively improving the data integrity verification efficiency.

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

[0128] The time window information needs to be clearly marked with the specific time period corresponding to the certificate to ensure that the subsequent data in the corresponding time period can be accurately located; the device identifier is used to distinguish the evidence data of different industrial devices to avoid confusion of multi-device data; the edge node digital signature needs to be generated by the node's private key to verify the legitimacy of the initiator of the evidence transaction to prevent fake 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.), and after verification, 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 tampered with unilaterally and always remains in a trusted state.

[0129] In addition, during the evidence storage process, it is also necessary to ensure that each link operation is strictly consistent with the data integrity verification strategy. For example, data grouping rules, hash algorithm selection, time window division, etc. all need to be consistent with the previous strategy setting to avoid subsequent verification failure due to operation deviation.

[0130] At the same time, the broadcast and consensus process of the evidence transaction needs to be monitored in real time. If network anomalies cause the transaction to fail to be successfully chained, 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 a foundation for the accuracy of subsequent device maintenance analysis.

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

[0132] In the embodiments of the present application, in order to ensure that the original running state data stream used by the device maintenance service provider for analysis is real and reliable, and to avoid drawing incorrect maintenance conclusions based on invalid data, the original running state data stream needs to be obtained from the edge computing node after the service provider initiates a maintenance analysis request and combined with the data integrity verification certificate in the distributed ledger to carry out integrity verification, so as to ensure the accuracy of subsequent device health diagnosis and maintenance scheme.

[0133] Specifically, first, when the device maintenance service provider initiates a maintenance analysis request, the edge computing node will retrieve the corresponding original running state data stream from the local storage according to the time range and device identifier specified in the request and return it to the service provider after responding to the request.

[0134] Further, after receiving the original running state data stream, the device maintenance service provider will calculate the local hash value of each data packet according to the same hash calculation frequency and grouping rule as when the original running state data stream was generated, and then reconstruct the Merkle tree according to these hash values, and finally calculate the reconstructed root hash value.

[0135] Further, the equipment maintenance service provider queries the data integrity verification credentials stored in the distributed ledger of the trusted data space according to the time range and the equipment identifier in the request.

[0136] Finally, the reconstructed root hash value is compared with the queried data integrity verification credentials: if they are consistent, it indicates that the original running state data stream has not been tampered with during storage and transmission, and the data integrity verification is passed; if they are inconsistent, it is determined that the verification fails and an alarm is triggered, prompting the equipment maintenance service provider that the data may be abnormal and the problem needs to be checked before maintenance analysis is carried out, so as to avoid the deviation of equipment health diagnosis caused by the use of tampered or damaged data, and ensure the scientificity and effectiveness of the subsequent targeted maintenance scheme.

[0137] The step S130 in the method provided by the embodiment of the application comprises:

[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 running state data stream from the local storage according to the time range and the equipment identifier specified in the request;

[0139] When the equipment maintenance service provider receives the original running state data stream, the local hash value of each data packet in the original running state data stream is recalculated according to the same hash calculation frequency and grouping rule as when it is generated, and a Merkle tree is reconstructed to obtain a reconstructed root hash value;

[0140] According to the time range and the equipment identifier, the data integrity verification credentials stored in the distributed ledger within the same time window are queried;

[0141] The reconstructed root hash value is compared with the data integrity verification credentials: if they are consistent, it is determined that the data integrity verification is passed, and if they are inconsistent, it is determined that the verification fails and an alarm is triggered.

[0142] In the embodiment of the application, in order to avoid the deviation of equipment health diagnosis caused by data problems, the invalidation of maintenance schemes, and the risk of industrial equipment failure caused by the above, after the service provider initiates a request, the original data is obtained by the edge computing node, the verification information is reconstructed, the trusted credentials are queried, and the comparison is completed, so as to realize the effective verification of the data integrity and provide a reliable data basis for subsequent equipment maintenance analysis.

[0143] Specifically, first, when the equipment maintenance service provider initiates a maintenance analysis request, after responding to the request, the edge computing node will retrieve and return the corresponding original running state data stream from the local storage according to the time range and the equipment identifier specified in the request.

[0144] Among them, the edge computing node as the local data storage and processing core of the factory, will store the original running state data stream in time dimension and equipment dimension, for example, save the data in the hierarchical directory of "equipment number-date-period", to ensure that after receiving the request, the complete data stream of the specified equipment in a specific period can be quickly located and extracted.

[0145] In addition, the accuracy of the time range needs to be consistent with the time window in the previous data processing, for example, if the previous data is processed in 1 minute as the time window, the time range in the request also needs to be accurate to the minute level, to avoid incomplete data retrieval due to time difference; the equipment identification needs to use a unique code, such as the equipment factory number, to ensure accurate matching of the target industrial equipment, to prevent data confusion with other equipment.

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

[0147] Among them, the hash calculation frequency needs to be completely consistent with the adaptive hash calculation frequency set in the previous period, for example, the previous hash value of a certain monitoring index is calculated at a frequency of 1 per 10 seconds, at this time, the received data stream also needs to be segmented and calculated according to the same frequency; the grouping rule also needs to be consistent with the previous period, such as the previous 5 sampling data of the same sensor are grouped into a data packet, at this time, the data needs to be grouped according to the same number and order, to ensure that the coverage range and data composition of each data packet are completely consistent with the generation.

[0148] The local hash value calculated in the same way can truly reflect the state of the current received data, and the reconstructed Merkle tree and the corresponding reconstructed root hash value calculated according to the same method can condense the integrity of multiple data packets into a unified verification identifier, laying the foundation for subsequent rapid comparison.

[0149] Further, the equipment maintenance service provider will query and obtain the data integrity verification credentials stored in the distributed ledger within the same time window according to the time range and equipment identification in the request.

[0150] Among them, the distributed ledger has the characteristics of decentralized storage and network consensus, and the verification credentials recorded in it cannot be tampered with unilaterally, and can be quickly indexed by timestamp and equipment identification. That is, after inputting the specified equipment identification and time window, the distributed ledger will return the corresponding data integrity verification credentials (i.e. the Merkle tree root hash value generated in the previous period) in that period, which is the core reference standard for judging whether the current received data is complete.

[0151] Further, the obtained reconstructed root hash value is compared with the queried data integrity verification voucher to determine whether the original running state data stream remains complete and has not been tampered with during storage and transmission.

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

[0153] On the contrary, if the two are inconsistent, it is determined that the data integrity verification fails, at which time an alarm needs to be triggered immediately, and the alarm information needs to clearly indicate that "data integrity verification fails, and there may be tampering or damage", and record key information such as failure time, device identifier and time window, to facilitate subsequent troubleshooting by technical personnel, such as checking whether there is a security vulnerability in the data transmission link, whether the local storage of the edge computing node is abnormal, etc.

[0154] Finally, through the above steps, a complete data integrity verification link of "data acquisition-reconstruction-checking-voucher query-comparison verification" is constructed, which not only ensures the reliability of the data used for maintenance analysis, but also provides a guarantee for the accuracy of predictive maintenance of industrial equipment, effectively reducing the maintenance risk caused by data problems.

[0155] S140: If the data integrity verification is passed, the device health status diagnosis and targeted maintenance are carried out according to the original running state data stream.

[0156] In the embodiments of the present application, in order to judge the health status of the device according to the actual device running data, avoid diagnosis deviation caused by data problems, and thus reduce the risk of device failure downtime and invalid maintenance cost, after the data integrity verification is passed, health diagnosis and customized maintenance are carried out based on the original running state data stream to ensure stable operation of the device, prolong the service life and support continuous production of the factory.

[0157] Specifically, first, the running data of each monitoring index of the device is extracted from the original running state data stream that passes the integrity verification, which covers the core parameters of the key subsystems of the device, such as the spindle temperature and speed of the machine tool, the exhaust pressure and lubricating oil level of the compressor, etc. When extracting, the data needs to be arranged in chronological order to form the running trend curve of each index to intuitively present the state change of the device in the preset time zone.

[0158] Further, the health status of the device is diagnosed based on the extracted monitoring index data in combination with the rated operating parameters of the device, historical fault data and industry maintenance standards.

[0159] Exemplarily, the actual operation temperature of the machine tool spindle is compared with the rated temperature range, if it continuously exceeds the rated value by more than 10%, it is determined that the spindle system has a health risk; by analyzing the fluctuation trend of the compressor exhaust pressure, if the short-term decline is more than 15% and accompanied by abnormal motor current, the air valve leakage fault can be warned.

[0160] In addition, during the diagnosis process, past similar fault cases also need to be associated, if the current data characteristics are consistent with the characteristics before the historical fault, the credibility of the diagnosis result is further improved.

[0161] Further, after the health status of the equipment is determined, a targeted maintenance scheme is formulated according to the diagnosis result.

[0162] Specifically, for the equipment with good health status but some monitoring indicators close to the warning threshold, a preventive maintenance scheme is formulated, such as regular cleaning and lubrication of key components; for the equipment with slight health risks, a repair maintenance scheme is formulated, such as replacing worn parts and adjusting parameter settings; for the equipment that has already failed, an emergency repair scheme is formulated, such as replacing the failed parts and coordinating the standby equipment to ensure the continuity of production.

[0163] Exemplarily, if the diagnosis finds that the barrel heating temperature fluctuation of a certain injection molding machine exceeds the normal range, and the historical data shows that this situation is mostly caused by the aging of the heating pipe, the targeted maintenance scheme includes: stopping after the current production batch is completed, replacing the same type of heating pipe, and checking the accuracy of the temperature sensor and the state of the barrel insulation layer to avoid related problems after maintenance.

[0164] In addition, after the maintenance is completed, the maintenance effect needs to be evaluated by comparing the barrel temperature data before and after the maintenance to ensure that the equipment returns to a normal healthy state and provides protection for subsequent stable operation.

[0165] The embodiments of the present application achieve the following technical effects through the specific implementation manner described above:

[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 credential generation and storage module 02 is configured to process real-time running state data stream of the industrial equipment according to the adaptive data integrity verification strategy on the edge computing node locally in the factory, generate data integrity verification credentials, and submit the data integrity verification credentials to the distributed ledger in the trusted data space for storage;

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

[0172] The equipment diagnosis and maintenance module 04 is configured to perform equipment health state diagnosis and targeted maintenance according to the original running state data stream if the data integrity verification is passed.

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

[0174] Based on the production plan of the factory in the preset time zone, the expected production task of the target industrial equipment and the expected product demand are screened; the equipment running load of the target industrial equipment is simulated according to the expected production task, and the predicted running load sequence is obtained; the data importance of a plurality of monitoring indexes of the target industrial equipment is evaluated according to the expected product demand and the predicted running load sequence, and a data importance set is output; and the hash calculation frequency and the on-chain storage frequency are set based on the data importance set and the real-time network state as the adaptive data integrity verification strategy.

[0175] Further, the verification strategy setting module 01 further comprises:

[0176] A data importance evaluation rule library is constructed, wherein the data importance evaluation rule library defines the importance weight of a plurality of monitoring indexes under different product demands and load levels; the predicted running load sequence is traversed, and for the load value of each time point, the data importance evaluation rule library is queried in combination with the expected product demand to obtain an importance score set of a plurality of monitoring indexes; and the importance score set is aggregated to generate a plurality of data importance of a plurality of monitoring indexes to construct a data importance set.

[0177] Further, the verification strategy setting module 01 further comprises:

[0178] The production load fluctuation analysis is performed on the predicted operation load sequence to obtain a production load fluctuation coefficient in a preset time zone, wherein the production load fluctuation coefficient is a standard deviation of load in the predicted operation load sequence and a mean value; the index fluctuation correlation analysis is performed on the plurality of monitoring indexes according to the production load fluctuation coefficient, and a plurality of index fluctuation correlation coefficients are output; the plurality of index fluctuation correlation coefficients are added respectively to obtain a plurality of data importance compensation coefficients; the plurality of data importance compensation coefficients are used to map and compensate a plurality of data importance to obtain a plurality of corrected data importance; and the plurality of corrected data importance and real-time network state are used to set a hash calculation frequency and a chain storage frequency as an adaptive data integrity verification strategy.

[0179] Further, the verification strategy setting module 01 further comprises:

[0180] The network communication performance parameters of the edge computing node to the trusted data space are monitored in real time, wherein the performance parameters include network delay and bandwidth utilization; the preset standard hash calculation frequency and the preset standard chain storage frequency under the current condition are obtained according to the network delay and the bandwidth utilization; the ratio of the plurality of corrected data importance to the mean value of the plurality of historical data importance of the same product production is calculated respectively to obtain a plurality of frequency optimization coefficients; the preset standard hash calculation frequency and the preset standard chain storage frequency are optimized respectively based on the plurality of frequency optimization coefficients to obtain an adaptive hash calculation frequency and an adaptive chain storage frequency as an adaptive data integrity verification scheme, and the adaptive data integrity verification strategy is generated according to a plurality of adaptive data integrity verification schemes.

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

[0182] The real-time operation state data stream is screened, sampled and grouped according to the plurality of adaptive data integrity verification schemes respectively to generate a plurality of to-be-processed data packets; the plurality of to-be-processed data packets are calculated respectively by using an encryption hash algorithm to generate a plurality of hash values; the plurality of hash values belonging to the same time window are constructed as a Merkle tree, and the root hash value of the Merkle tree is calculated as a data integrity verification credential of the time window; the data integrity verification credential, time window information, device identifier and edge node digital signature are packaged into a storage transaction, broadcast to a distributed ledger in the trusted data space, recorded in a new block after network consensus, and the storage is completed.

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

[0184] When the device 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 running state data stream from the local storage according to the time range and device identifier specified in the request; when the device maintenance service provider receives the original running state data stream, the local hash value of each data packet in the original running state data stream is recalculated according to the same hash calculation frequency and grouping rule as when it is generated, and the Merkle tree is reconstructed to calculate the reconstructed root hash value; according to the time range and device identifier, the data integrity verification voucher stored in the same time window is queried from the distributed ledger; the reconstructed root hash value is compared with the data integrity verification voucher, if they are consistent, it is determined that the data integrity verification is passed, if they are inconsistent, it is determined that the verification fails and an alarm is triggered.

[0185] Embodiment three, as shown in the attached Figure 3 Based on the inventive concept of the data integrity joint verification method of a trusted data space provided in embodiment one, the present application further provides a computer readable storage medium 300 having a first computer program 311 stored thereon.

[0186] When the first computer program 311 is executed by the processor 420, the data integrity joint verification method of a trusted data space in embodiment one is realized.

[0187] Embodiment four, as shown in the attached Figure 4 Based on the inventive concept of the data integrity joint verification method of a trusted data space provided in embodiment one, the present application further provides a verification terminal 400, which is composed of:

[0188] The memory 410 is used to store the second computer program 411.

[0189] The 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, the data integrity joint verification method of a trusted data space in embodiment one is realized.

[0191] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. And the above-mentioned describes the specific embodiments of the present application. In addition, the processes depicted in the attached drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0192] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0193] The specification and drawings are only exemplary of the application and are to be regarded as covering all modifications, variations, combinations or equivalents that are within the scope of the present application. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application encompass all such modifications and changes as fall within the scope of the application and its equivalents.

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; Among them, 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. Specifically, 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 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. Among them, the hash calculation frequency and on-chain evidence storage frequency are set based on the importance of the aforementioned correction data and the real-time network status, 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.

2. The method for joint verification of data integrity in a trusted data space according to claim 1, 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.

3. The method for joint verification of data integrity in a trusted data space according to claim 1, 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.

4. 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.

5. A joint verification system for data integrity in 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 1-4, 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.

6. 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 1-4.

7. 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 1-4.

Images