Engineering supervision data collaborative management method based on Internet of Things

Abstract

The invention relates to the technical field of engineering management, in particular to an engineering supervision data collaborative management method based on the Internet of Things, which comprises the following steps: acquiring multi-source engineering data in real time through an Internet of Things sensing terminal deployed at a construction site, and generating a multi-source engineering data set; when it is detected that the key parameter combination exceeds the limit, a dynamic permission strategy of three-party operation permission is automatically generated according to the current construction stage feature, and a multi-party cooperation task request is triggered; and distributing the collaborative task request to the corresponding terminal through the message middleware, and synchronously executing collaborative operation. According to the method, event processing time limit grading and operation white list generation are supported, the precision and efficiency of project abnormal response are improved, the problems of fuzzy responsibility and response delay are avoided, the data security, verifiability and supervision compliance of collaborative tasks of all construction parties are improved, and the method is particularly suitable for multi-project concurrency and cross-organization collaborative scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering management, and in particular to a collaborative management method for engineering supervision data based on the Internet of Things. Background Art

[0002] During the construction of large-scale projects, project supervision, as an important means to ensure construction quality, safety and progress, is facing increasingly severe challenges in terms of its digitalization level and collaborative response capabilities. The current mainstream project supervision systems mostly use regular inspections and manual data entry as the main means, and have problems such as data lag, untimely response to exceptions, and blurred responsibility boundaries. They are unable to adapt to the needs of high-frequency monitoring and real-time linkage in complex construction environments.

[0003] On the one hand, with the gradual application of Internet of Things technology in construction sites, sensor data such as concrete stress, tower crane posture, dust and noise are becoming increasingly abundant. However, due to the large differences in communication protocols of various types of equipment, different data frequencies, and lack of unified identification of spatial locations, multi-source engineering data is difficult to integrate and use, affecting the integrity and availability of monitoring data.

[0004] On the other hand, traditional engineering supervision methods generally adopt static threshold judgment and unilateral review processes, lacking a complex event identification mechanism for dynamic changes in the construction phase, and also lacking a linkage mechanism and dynamic authority control strategy between the supervision party, the construction party and the construction party. This often results in slow response to emergencies, distorted information transmission, and even the risk of coordination failure. Summary of the Invention

[0005] The present invention provides a collaborative management method for engineering supervision data based on the Internet of Things, combining hierarchical event detection with a multi-party authority collaboration strategy, and introducing a trusted evidence storage mechanism to improve the real-time, collaborative, and security of supervision data, and support intelligent management of quality, safety, and risks throughout the entire engineering process.

[0006] The collaborative management method of engineering supervision data based on the Internet of Things includes the following steps: S1. Real-time collection of multi-source engineering data through IoT sensing terminals deployed at the construction site. The IoT sensing terminals include concrete stress sensors, tower crane attitude monitors, and dust and noise detectors. The collected multi-source engineering data is parsed across protocols to generate a multi-source engineering data set. S2. Real-time scanning of multi-source engineering data sets based on an event threshold library. When a critical parameter combination is detected as exceeding the limit, a dynamic permission policy is automatically generated based on the characteristics of the current construction phase, including the operation permissions of the supervisor, contractor, and builder, and triggering a multi-party collaborative task request. S3. Distribute the collaborative task request to the corresponding terminal through the message middleware, and synchronously perform the following collaborative operations: Push decision-making work orders including abnormal data comparison graphs to the supervision engineer's terminal; Send equipment limit control instructions and maintenance work orders to the construction team terminal; Upload the encrypted original data packet and processing log to the supervision platform; All operation results are written into the blockchain collaborative evidence chain after being digitally signed by multiple parties.

[0007] Optionally, the S1 specifically includes: S11. Build a multi-protocol adaptation layer to dynamically analyze the Modbus-RTU protocol data of the concrete stress sensor, the CAN bus data of the tower crane attitude monitor, and the LoRaWAN protocol data of the dust and noise detector. Add timestamps and device geotags to Modbus-RTU protocol data; Convert the CAN bus data frame into a standardized JSON structure including the working status code; Perform packet loss compensation on LoRaWAN data packets; S12. Perform spatiotemporal alignment of multimodal data: Establish a benchmark time axis and unify data with different sampling frequencies to a time granularity of 0.5 seconds; Downsample the tower crane attitude data and perform sliding window mean alignment with the dust data; Based on the BIM model coordinate system - spatial benchmark, add a three-dimensional position code to each data point; S13. Generate a structured multi-source engineering data set, including: Timestamp (ISO 8601 standard format); Device ID (based on GB / T 25647 encoding specification); Engineering parameters (stress value, inclination angle, PM2.5, etc. with unit dimensions).

[0008] Optionally, the S2 specifically includes: S21. Build a multi-dimensional event threshold library, including an earthwork stage threshold group, a main stage threshold group, and a decoration stage threshold group; S22. Use a sliding time window mechanism to perform real-time scanning of multi-source engineering data sets: For high-frequency data (crane posture > 10Hz), a 1-second rolling window is used for short-term trend analysis. Construct a 24-hour sliding window for low-frequency data (concrete stress < 1Hz) to evaluate the long-term rate of change When two or more parameters are detected to be out of limit at the same time, a compound event alarm is triggered; S23. Dynamic permission strategy generation algorithm: Determine the weight coefficient of the current construction stage according to the construction progress , based on the event type matching preset permission template library, calculate the three-party operation permission matrix; S24. Generate a multi-party collaborative task request message.

[0009] Optionally, in the earthwork stage threshold group: the cumulative displacement of the foundation pit is greater than 30 mm and the support axial force mutation rate is greater than 15% / h; In the threshold group of the main stage: the settlement difference between adjacent formworks is greater than 1 / 400 of the span value and the concrete strength growth rate is less than 0.5 MPa / h; In the threshold group of the renovation stage: indoor TVOC concentration>0.6mg / m³ for 2 hours and noise value>75dB(A).

[0010] Optionally, the three-party operation authority matrix is ​​expressed as: ; in, is the event level coefficient (including major / moderate / general), is the weight of the current construction stage, obtained from BIM simulation, is the weight of the current construction party's operation accuracy (range 0.7-1.3), is the basic value of the construction party's authority, γ = 0.8 to 1.0, with a default value of 0.9, which can be fine-tuned according to the project management level and supervision requirements. is the time decay factor, controlling the validity of authority, is the authority decay rate constant, .

[0011] Optionally, the multi-party collaborative task request message includes an event code, a three-dimensional geographic fence of the impact range, a processing time limit classification, and an operation permission whitelist. The three-party operation permission matrix determines the operation permission level of the three parties (supervisor, construction party, and construction party) in the current complex event, and the message distributes the corresponding task content and execution authority based on the matrix to ensure that each party can only perform operations that match its permission level.

[0012] 1. Event code: The operation authority level of each party in the matrix ( ) corresponding event level Launched, matching the standard coding in GB / T50941-2017.

[0013] 2. 3D geo-fencing of impact range: Extract the location data of those involved in equipment control (e.g., construction) from the permission matrix to determine the spatial scope of the response operation and construct the fence based on the BIM coordinate system.

[0014] 3. Processing time limit classification: Based on the highest authority level item in the matrix ( ) represents the event severity level and the corresponding processing time limit (Level 1 , Level 2 , level 3 ).

[0015] 4. Operation permission whitelist: The corresponding whitelist entry is directly generated by the party with non-zero permission value in the matrix and is bound to the signature key of the corresponding terminal or person.

[0016] Optionally, the S3 specifically includes: S301, configure a hierarchical message routing strategy based on the message middleware: Set message expiration time and use RabbitMQ dead letter queue to handle timeout work orders; Configure a dedicated MQTT topic for device control instructions; When the evidence data is written to the Kafka partition, the construction section number and time serial number are attached; S32, build a decision-making work order intelligent push module: Extract abnormal data comparison graph features and encapsulate work order data packets in lightweight WebAssembly format; S33, implements safety interlocking of equipment control instructions, and limit control instructions include a double verification mechanism: Dynamically generate device fingerprint code by combining PLC serial number + MAC address hash value; Embedded timeliness control parameters, including validity period less than 10 minutes, and automatic expiration after timeout; S34, build a blockchain collaborative evidence chain.

[0017] Optionally, the extraction of abnormal data comparison graph features in S32 includes: Current value - threshold deviation; Set a sliding time window to generate a comparison curve of historical data for the same period; Obtain a three-dimensional space influence range heat map based on BIM model rendering.

[0018] Optionally, the step S34 of constructing a blockchain collaborative evidence chain includes: S341, the original data packet encryption adopts SM4 national encryption algorithm; S342, the structured storage fields of the operation log include the operation type, digital signature summary, and timestamp chain; S343, construct a Merkle tree-shaped evidence storage structure, where: Parent node hash value = Hash(Supervision Operation Log || Construction Execution Log) The sub-nodes include regulatory audit trail validation data.

[0019] Beneficial effects of the present invention: The present invention proposes a multi-dimensional composite event detection and dynamic authority coordination mechanism for the entire construction cycle to enhance cross-role linkage response capabilities. A multi-dimensional event threshold library covering the three stages of earthwork, main body, and decoration is constructed. Based on a dynamic monitoring mechanism of composite condition combination and time window, it supports real-time identification of multi-parameter collaborative over-limit events. By introducing construction progress weights, event levels, and time attenuation factors to calculate the three-party operation authority matrix, differentiated, phased authority control, and task distribution between the supervisor, the construction party, and the builder are achieved. This mechanism supports event processing time limit classification and operation whitelist generation, improving the accuracy and efficiency of engineering abnormality responses and avoiding problems of unclear responsibilities and delayed responses.

[0020] The present invention introduces a lightweight message middleware mechanism in the task collaboration process, performs hierarchical routing and evidence marking on control instructions, data anomalies and maintenance tasks, and combines digital signatures with time control to realize identity authentication and time limit locking of the task process. All original data and operation logs are written into the blockchain collaborative evidence chain based on the MerklePatricia Trie structure after SM4 encryption and three-party key decentralized management, effectively preventing single-point tampering and audit omissions. Compared with traditional supervision data platforms, this method improves the data security, verifiability and regulatory compliance of collaborative tasks among all parties in the construction, and is particularly suitable for multi-project concurrency and cross-organizational collaboration scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic diagram of a collaborative management method according to an embodiment of the present invention; Figure 2 Schematic diagram of collaborative operations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0024] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0025] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0026] like Figure 1-Figure 2 As shown in FIG, the collaborative management method of engineering supervision data based on the Internet of Things includes the following steps: S1. Real-time collection of multi-source engineering data is achieved through IoT sensing terminals deployed at the construction site. These terminals include concrete stress sensors, tower crane attitude monitors, and dust and noise detectors. The collected multi-source engineering data is parsed across protocols to generate a multi-source engineering data set. S2. Real-time scanning of multi-source engineering data sets based on an event threshold library. When a critical parameter combination is detected as exceeding the limit, a dynamic permission policy is automatically generated based on the characteristics of the current construction phase, including the operation permissions of the supervisor, contractor, and builder, and triggering a multi-party collaborative task request. S3. Distribute the collaborative task request to the corresponding terminal through the message middleware, and synchronously perform the following collaborative operations: Push decision-making work orders including abnormal data comparison graphs to the supervision engineer's terminal; Send equipment limit control instructions and maintenance work orders to the construction team terminal; Upload the encrypted original data packet and processing log to the supervision platform; All operation results are written into the blockchain collaborative evidence chain after being digitally signed by multiple parties.

[0027] S1 specifically includes: S11. Build a multi-protocol adaptation layer to dynamically analyze the Modbus-RTU protocol data of the concrete stress sensor, the CAN bus data of the tower crane attitude monitor, and the LoRaWAN protocol data of the dust and noise detector. Add timestamps and device geotags to Modbus-RTU protocol data; Convert the CAN bus data frame into a standardized JSON structure including the working status code; Perform packet loss compensation on LoRaWAN data packets; S12. Perform spatiotemporal alignment of multimodal data: Establish a benchmark time axis and unify data with different sampling frequencies to a time granularity of 0.5 seconds; Downsample the tower crane attitude data and perform sliding window mean alignment with the dust data; Based on the BIM model coordinate system - spatial benchmark, add a three-dimensional position code to each data point; S13. Generate a structured multi-source engineering data set, including: Timestamp (ISO 8601 standard format); Device ID (based on GB / T 25647 encoding specification); Engineering parameters (stress value, inclination angle, PM2.5, etc. with unit dimensions).

[0028] S2 specifically includes: S21, build a multi-dimensional event threshold library, including: Earthwork Phase Threshold Group: ,in, is the cumulative displacement of the foundation pit, is the axial force of the supporting structure; Main stage threshold group: ,in, Formwork settlement difference is the span length, is the concrete strength; Renovation Stage Threshold Group: ,in, is the TVOC concentration in indoor air, is the equivalent noise level.

[0029] S22 uses a sliding time window mechanism to perform real-time scanning of multi-source engineering data sets: High-frequency data (e.g. tower crane attitude sampling rate > 10 Hz): Use a 1-second rolling window to perform short-term trend statistics; Low-frequency data (e.g. concrete stress sampling rate <1 Hz): Use a 24-hour sliding window to assess the long-term rate of change; Compound event trigger mechanism: If two or more groups of parameters exceed the limit simultaneously, it is determined to be a compound event, which is expressed as: ,in, is the current multi-source engineering monitoring parameter set, Represents The corresponding threshold, represents any two real-time engineering monitoring parameters (stress, displacement, noise, etc.) selected from the monitoring parameter set, It is a logical "AND" operator, which means that two conditions must be met at the same time. Is an existential quantifier, indicating that there is at least one set of parameter combinations that meet the conditions. Indicates logical implication, indicating that a compound event alarm is triggered when the conditions are met. Trigger Alert indicates the operation action in which the system determines it as a compound event and issues an alarm signal.

[0030] S23, dynamic permission strategy generation algorithm: According to the construction progress stage, the construction progress curve is extracted from the BIM4D model to determine the weight coefficient of the current stage : ; Generation of tripartite operation authority matrix: ; in, is the event level coefficient (including major / moderate / general), is the construction stage weight, obtained from BIM simulation, is the construction party's operation accuracy weight (range 0.7-1.3), is the basic value of the construction party's authority, γ = 0.8 to 1.0, with a default value of 0.9, which can be fine-tuned according to the project management level and supervision requirements. is the time decay factor, controlling the validity of authority, is the authority decay rate constant, .

[0031] S24, generating a structured collaborative task request message, including: Event coding (following GB / T50941-2017 Engineering Event Classification Standard); 3D geo-fence of the impact range (generated based on BIM model coordinates); Processing time limit classification: ; Operation permission whitelist (including device control signature key set).

[0032] S3 specifically includes: S31, the message middleware configures a hierarchical message routing strategy: Timeout work order processing mechanism: Use RabbitMQ dead letter queue mechanism and set message expiration time TTL: ; Device control command publishing: Configure a dedicated MQTT topic channel for device control commands and set the following parameters: , It is the MQTT service quality level, the value range is: 0, 1, 2, Indicates the MQTT reserved message flag; The evidence data flows into Kafka: each piece of data is appended with an identification field: ,in, Indicates the construction section number. Indicates the time serial number of data generation, Represents the Kafka partition key, used for data writing to partition routing.

[0033] S32, build a decision-making work order intelligent push module: S321, abnormal data comparison graph feature extraction: The relative deviation between the current value and the threshold : , Vactual is the actual collected data value, Vthreshold is the threshold of the corresponding parameter, and Window hist is the length of the sliding time window; The comparison of historical data for the same period uses a sliding time window: ; 3D spatial impact range heat map: generated by BIM model rendering, combining abnormal data coordinates and impact radius mapping; S322, work order packaging format: encapsulated in WebAssembly (WASM) format, with embedded AR positioning anchor data.

[0034] S33, implement equipment control instruction safety interlock: S331, limit control instruction, includes two levels of safety verification: Dynamic device fingerprint generation: ,in, Represents the device fingerprint hash value, Indicates the first 6 digits of the PLC device serial number. Indicates the 4th to 8th bits of the device network card MAC address. Indicates the construction instruction number of the day; Embed the timing parameters of the control instructions: , To control the effective duration of the instruction; The instruction will be automatically invalidated after the timeout.

[0035] S332, maintenance work order watermark, work order with digital watermark Contents include: ,in, Indicates the construction team number, Indicates the GPS coordinate stamp of the positioning time point.

[0036] S34, building a blockchain collaborative evidence chain: S341, data encryption method: using SM4 national encryption algorithm, CBC mode, and the key adopts a distributed storage solution: ,in, Indicates the complete key used for SM4 encryption. They are kept by the supervisor, the construction party and the supervisor respectively. It is a bitwise XOR operation to ensure three-party collaborative decryption; S342, the operation log field structure includes: Operation type code (in accordance with GB / T32911-2016); Digital Signature Summary: ; High-precision timestamp: time accuracy ; S343, blockchain evidence storage structure, builds a MerklePatriciaTrie (MPT) tree structure, and organizes operation log data: Parent node hash value generation method: , Represents the hash value of the Merkle tree parent node, Indicates the content of the supervisor's operation log. Indicates the content of the construction party's operation log. Indicates the concatenation operation of string or binary data, also known as the connection operator. The above expression means that the operation log of the supervisor Operation log with the construction party Concatenate them together and calculate the hash value of the concatenated result as the parent node hash in the Merkle tree.

[0037] The sub-node contains the regulator's operation path and audit verification data.

[0038] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0039] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. The collaborative management method of engineering supervision data based on the Internet of Things is characterized by: The following steps are involved: S1. Real-time collection of multi-source engineering data through IoT sensing terminals deployed at the construction site. The IoT sensing terminals include concrete stress sensors, tower crane attitude monitors, and dust and noise detectors. The collected multi-source engineering data is parsed across protocols to generate a multi-source engineering data set. S2. Real-time scanning of multi-source engineering data sets based on an event threshold library. When a critical parameter combination is detected as exceeding the limit, a dynamic permission policy is automatically generated based on the characteristics of the current construction phase, including the operation permissions of the supervisor, contractor, and builder, and triggering a multi-party collaborative task request. S3. Distribute the collaborative task request to the corresponding terminal through the message middleware, and synchronously perform the following collaborative operations: Push decision-making work orders including abnormal data comparison graphs to the supervision engineer's terminal; Send equipment limit control instructions and maintenance work orders to the construction team terminal; Upload the encrypted original data packet and processing log to the supervision platform; All operation results are written into the blockchain collaborative evidence chain after being digitally signed by multiple parties.

2. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 1 is characterized in that: Said S1 specifically includes: S11. Build a multi-protocol adaptation layer to dynamically analyze the Modbus-RTU protocol data of the concrete stress sensor, the CAN bus data of the tower crane attitude monitor, and the LoRaWAN protocol data of the dust and noise detector. Add timestamps and device geotags to Modbus-RTU protocol data; Convert the CAN bus data frame into a standardized JSON structure including the working status code; Perform packet loss compensation on LoRaWAN data packets; S12. Perform spatiotemporal alignment of multimodal data: Establish a benchmark time axis and unify data with different sampling frequencies to a time granularity of 0.5 seconds; Downsample the tower crane attitude data and perform sliding window mean alignment with the dust data; Based on the BIM model coordinate system - spatial benchmark, add a three-dimensional position code to each data point; S13. Generate a structured multi-source engineering data set, including: Timestamp; Device ID; Engineering parameters.

3. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 1 is characterized in that: The S2 specifically includes: S21. Build a multi-dimensional event threshold library, including an earthwork stage threshold group, a main stage threshold group, and a decoration stage threshold group; S22. Use a sliding time window mechanism to perform real-time scanning of multi-source engineering data sets: Use a 1-second rolling window for short-term trend analysis of high-frequency data Construct a 24-hour sliding window for low-frequency data to evaluate the long-term change rate When two or more parameters are detected to be out of limit at the same time, a compound event alarm is triggered; S23. Dynamic permission strategy generation algorithm: Determine the weight coefficient of the current construction stage according to the construction progress , based on the event type matching preset permission template library, calculate the three-party operation permission matrix; S24. Generate a multi-party collaborative task request message.

4. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 3 is characterized in that: In the earthwork stage threshold group: the cumulative displacement of the foundation pit is greater than 30 mm and the sudden change rate of the supporting axial force is greater than 15% / h; In the threshold group of the main stage: the settlement difference between adjacent formworks is greater than 1 / 400 of the span value and the concrete strength growth rate is less than 0.5 MPa / h; In the threshold group of the renovation stage: indoor TVOC concentration>0.6mg / m³ for 2 hours and noise value>75dB(A).

5. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 3 is characterized in that: The tripartite operation authority matrix is ​​expressed as: ； in, is the event level coefficient, is the weight of the current construction stage, is the weight of the current construction party’s operation accuracy, is the base value of the builder's authority. is the time decay factor, controlling the validity of authority, is the authority decay rate constant.

6. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 3 is characterized in that: The multi-party collaborative task request message includes an event code, a three-dimensional geographic fence of an impact range, a processing time limit classification, and an operation permission whitelist.

7. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 1 is characterized in that: The S3 specifically includes: S301, configure a hierarchical message routing strategy based on the message middleware: Set message expiration time and use RabbitMQ dead letter queue to handle timeout work orders; Configure a dedicated MQTT topic for device control instructions; When the evidence data is written to the Kafka partition, the construction section number and time serial number are attached; S32, build a decision-making work order intelligent push module: Extract abnormal data comparison graph features and encapsulate work order data packets in lightweight WebAssembly format; S33, implements safety interlocking of equipment control instructions, and limit control instructions include a double verification mechanism: Dynamically generate device fingerprint code by combining PLC serial number + MAC address hash value; Embedded timeliness control parameters, including validity period less than 10 minutes, and automatic expiration after timeout; S34, build a blockchain collaborative evidence chain.

8. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 7 is characterized in that: The extraction of abnormal data comparison graph features in S32 includes: Current value - threshold deviation; Set a sliding time window to generate a comparison curve of historical data for the same period; Obtain a three-dimensional space influence range heat map based on BIM model rendering.

9. The collaborative management method for engineering supervision data based on the Internet of Things according to claim 8 is characterized in that: The S34 construction of blockchain collaborative evidence chain includes: S341, the original data packet encryption adopts SM4 national encryption algorithm; S342, the structured storage fields of the operation log include the operation type, digital signature summary, and timestamp chain; S343, construct a Merkle tree-shaped evidence storage structure, where: Parent node hash value = Hash(Supervision Operation Log || Construction Execution Log) The sub-nodes include regulatory audit trail validation data.

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