Photovoltaic project construction information management system based on industrial internet

Abstract

The invention relates to the technical field of data processing and project management, in particular to a photovoltaic project construction information management system based on the industrial internet, and the system comprises a data collection module which is used for capturing an original data packet from a construction site; the data decision value evaluation module is used for performing decision value calculation on the original data packet captured by the data acquisition module to obtain a data decision value score; the dynamic resource allocation module is used for allocating the data packet to a preset transmission channel according to the data decision value score calculated by the data decision value evaluation module, and generating a transmission decision; and the cost-benefit optimization module is used for dynamically adjusting decision value calculation in response to the actual communication cost and the actual data packet loss rate which are generated by the transmission decision, so that closed-loop adaptive adjustment is formed. And effective control of the total communication cost is realized.

Description

Technical Field

[0001] This invention relates to the field of data processing and project management technology, specifically to a photovoltaic project construction information management system based on the Industrial Internet. Background Technology

[0002] The construction of large-scale distributed photovoltaic power stations is characterized by dispersed work sites, complex environments, multiple trades involved, and long construction periods. During construction, massive amounts of heterogeneous data, such as drone inspection videos, IoT sensor data, construction personnel status information, material entry and exit records, and key process acceptance reports, need to be transmitted in real time or near real time between various dispersed construction sites and the central command center.

[0003] Existing technologies typically employ two extreme strategies to address such issues: one is a high-reliability fixed network strategy, which aims for extremely low packet loss rates by deploying high-cost facilities such as fiber optics or full coverage of 5G / satellite communications, but this is not economically viable; the other is a low-cost best-effort strategy, which uses public wireless networks or unlicensed frequency bands, but cannot guarantee data transmission stability when the network is congested, leading to delays or loss of critical construction information, causing ineffective waiting between work processes, and seriously affecting project progress.

[0004] Therefore, existing technologies generally face an unresolved technical dilemma: how to significantly reduce the construction and operation costs of IT infrastructure while ensuring the effective transmission of critical data and keeping the packet loss rate within an acceptable range. Summary of the Invention

[0005] The purpose of this invention is to provide a photovoltaic project construction information management system based on the Industrial Internet, which solves the problems existing in the background technology.

[0006] To address the aforementioned technical problems, this invention provides a photovoltaic project construction information management system based on the Industrial Internet, comprising: The data acquisition module is used to capture raw data packets from the construction site; The data decision value assessment module is used to calculate the decision value of the raw data packets captured by the data acquisition module and obtain the data decision value score. The dynamic resource allocation module is used to allocate data packets to preset transmission channels and generate transmission decisions based on the data decision value score calculated by the data decision value assessment module. The cost-benefit optimization module dynamically adjusts the decision value calculation in response to the actual communication cost and actual data packet loss rate resulting from transmission decisions, forming a closed-loop adaptive adjustment.

[0007] The preferred method for calculating decision value is as follows: Obtain the critical path impact factor, data type urgency, and downstream process dependency of the construction task associated with the data packet; weight the critical path impact factor, data type urgency, and downstream process dependency according to the preset weight coefficients, and perform attenuation calculation by combining the data timeliness parameter calculated from the data packet generation time to obtain the data decision value score.

[0008] Preferably, the process for obtaining the critical path impact factor is as follows: Determine whether the construction task associated with the data packet is located on the project critical path preset by the project management software; If the construction task is located on the critical path of the project, the critical path impact factor is set to the preset maximum impact value. If the construction task is a non-critical task, the critical path impact factor is calculated using a normalized inverse function based on the total float time of the construction task obtained from the project management software.

[0009] Preferably, the allocation process of the dynamic resource allocation module is as follows: The data decision value score is compared and analyzed with preset high-value thresholds and low-value thresholds; If the data decision value score is greater than or equal to the high value threshold, the data packet will be allocated to the high reliability channel. If the data decision value score is less than the high value threshold but greater than or equal to the low value threshold, the data packet will be allocated to the standard commercial channel. If the data decision value score is less than the low value threshold, the data packet will be allocated to a low-cost IoT channel or placed in a delayed transmission queue.

[0010] The preferred process for setting the high-value threshold is as follows: The cumulative distribution function of data decision value scores in historical statistical data is used; a high-value threshold is set at a preset quantile, and the total amount of data above this quantile is matched with the secure bandwidth of the high-reliability channel.

[0011] The preferred process for setting the low-value threshold is as follows: Based on the cost preference coefficient set by the system administrator, determine the proportion of data to be sent late; maintain the probability density function of the data decision value score in real time, and set the low value threshold to a position where the proportion of data with a value lower than its value in the total data volume is equal to the proportion of data to be sent late.

[0012] Preferably, the dynamic adjustment process of the cost-benefit optimization module is as follows: A system status monitoring module is introduced to measure the actual total communication cost and the weighted average actual data packet loss rate within an evaluation cycle. The actual total communication cost is compared with the baseline communication cost to obtain the cost item. The weighted average actual data packet loss rate is compared with the target data packet loss rate upper limit set by the administrator. If the weighted average actual data packet loss rate exceeds the target data packet loss rate upper limit, the difference between the two is squared to obtain the performance penalty item. If it does not exceed the limit, the performance penalty item is set to zero. Based on the preset cost preference coefficient and performance preference coefficient, the cost item and performance penalty item are weighted and combined to calculate the cost-benefit objective function value.

[0013] Preferably, the cost-benefit optimization module is further used for: The optimal weight coefficients are obtained by minimizing the cost-benefit objective function. These optimal weight coefficients are then sent to the data decision value assessment module to update the preset weight coefficients corresponding to the critical path impact factor, data type urgency, and downstream process dependence, thus achieving dynamic adjustment.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) In a specific application scenario, assuming a data packet of completion of the pile foundation pouring task associated with the critical path of the project is generated, the dynamic resource allocation module will immediately allocate it to a high-reliability satellite communication channel for transmission. The module will allocate it to a low-cost IoT channel or place it in a delayed transmission queue. This solution avoids paying expensive satellite channel fees for this routine inspection photo and also avoids the loss of critical pouring completion information due to network congestion. Thus, while ensuring that the project proceeds as planned, the unnecessary data transmission cost is reduced to the minimum, and the total IT cost is significantly reduced.

[0015] (2) Improve project management and collaborative work efficiency, eliminate the waste of working hours caused by poor information flow. After the high-value data package is reliably and timely transmitted to the central command center after the pouring is completed, the project management system can instantly trigger the next process. Through its data decision value assessment module, the system can accurately identify the value of information and ensure reliable transmission, thus ensuring seamless connection between processes and directly transforming the smooth flow of information into the guarantee of project progress and cost savings.

[0016] (3) It enables the system to adapt from passive response to active optimization, realizes the dynamic optimal matching of project goals and IT resources, and makes the system no longer a static rule executor, but an intelligent system that can actively learn and adapt to the dynamic changes of the project and continuously seek the best balance between cost and performance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a logic block diagram of the system of the present invention; Figure 2 This is a logical flowchart for calculating the decision value of this invention; Figure 3 This is a logical block diagram for obtaining the critical path impact factor of this invention; Figure 4 This is a logic block diagram of the dynamic resource allocation module of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Example 1 Please see Figure 1 This invention provides a photovoltaic project construction information management system based on the Industrial Internet, comprising: The data acquisition module is used to capture raw data packets from the construction site; The data decision value assessment module is used to calculate the decision value of the raw data packets captured by the data acquisition module and obtain the data decision value score. The dynamic resource allocation module is used to allocate data packets to preset transmission channels and generate transmission decisions based on the data decision value score calculated by the data decision value assessment module. The cost-benefit optimization module dynamically adjusts the decision value calculation in response to the actual communication cost and actual data packet loss rate resulting from transmission decisions, forming a closed-loop adaptive adjustment.

[0020] In one specific embodiment of the present invention, a photovoltaic project construction information management system based on the Industrial Internet is used to solve the technical contradiction between the stability of data transmission and the economy of communication facilities in the construction scenario of large-scale distributed photovoltaic power stations. In one embodiment of the system, its internal modules constitute a closed-loop adaptive control system. The data acquisition module captures heterogeneous data packets from devices such as drones, IoT sensors, and personnel terminals at the construction site. Subsequently, the data decision value assessment module performs quantitative calculation of the decision value of each data packet and produces a data decision value score. The dynamic resource allocation module assigns a preset physical transmission channel to the data packet based on the score and generates a transmission decision. The adaptive adjustment function of the system is implemented by the cost-benefit optimization module, which continuously monitors the actual total communication cost and weighted average data packet loss rate caused by the execution of transmission decisions, and adjusts the internal calculation parameters of the data decision value assessment module in reverse based on the monitoring results. This architecture accurately supplies limited high-reliability network resources to key data that have a decisive impact on project objectives, thereby achieving effective control of total communication costs while ensuring that project progress is not affected by information transmission quality. Example 2 Please see Figure 2 The process of calculating decision value is as follows: Obtain the critical path impact factor, data type urgency, and downstream process dependency of the construction task associated with the data packet; weight the critical path impact factor, data type urgency, and downstream process dependency according to the preset weight coefficients, and perform attenuation calculation in combination with the data timeliness parameter calculated from the data packet generation time to obtain the data decision value score. Please see Figure 3 The process for obtaining the critical path impact factor is as follows: Determine whether the construction task associated with the data packet is located on the project critical path preset by the project management software; If the construction task is located on the critical path of the project, the critical path impact factor is set to the preset maximum impact value. If the construction task is a non-critical task, the critical path impact factor is calculated using a normalized inverse function based on the total float time of the construction task obtained from the project management software.

[0021] In a preferred embodiment of the present invention, the data decision value assessment module calculates the data decision value score of each data packet using a multi-dimensional quantitative assessment model. The technical implementation path of this module in performing decision value calculation lies in obtaining three core value dimensions: first, the critical path impact factor, which is determined by parsing the construction tasks associated with the data packet content from the project management software and judging whether they are on the project's critical path; second, the data type urgency, which is obtained by matching from the system's built-in configurable query table based on the business attributes of the data itself. Specifically, the query table assigns a non-negative 'basic priority score' to each preset data type (e.g., personnel safety alarms, critical process acceptance reports, routine inspection photos, etc.). The system will iterate through all preset data types and obtain the highest priority score as the 'maximum priority score'. The lowest priority score is used as the 'minimum priority score'. Data type urgency This is achieved by performing min-max normalization on the basic priority score, ensuring that its value is accurately mapped to... Within the range.

[0022] To ensure the feasibility of the technical solution, the urgency of data types is considered. The calculation formula is as follows: when equal At that time, all data types Set all values ​​to 0 or 0.5. For example, you can set a 'personnel safety alarm'. The score is 100 for the 'Key Process Acceptance Report'. The value is 70, 'routine inspection photos' It is 20. At this time, It is 100. The value is 20. Therefore, these three types of data... Calculated separately as: security alarms Acceptance report ; Regular inspection photos This approach provides a strictly normalized, clear, and quantifiable urgency evaluation standard for data with different business attributes.

[0023] Third, the dependence of downstream processes is quantified by analyzing the logical relationship network of processes in project management software; To ensure the feasibility of the technical solution, the process of obtaining the critical path impact factor is designed as a deterministic assignment logic. The data decision value assessment module first judges the construction task. If the task is marked as being on the critical path by the project management software, its critical path impact factor is assigned the maximum impact value of 1. Otherwise, if it is determined to be a non-critical task, the module retrieves the total float time of the task from the project management software and calculates it using a normalized inverse function. The data decision value assessment module uses a set of dynamically adjustable weight coefficients to perform a weighted summation of the three influencing factors mentioned above, and introduces a timeliness parameter calculated based on data packet timestamps. It then performs an exponential decay calculation on the weighted result to finally obtain a normalized data decision value score. This calculation process is defined by the data decision value model formula, which aims to create a mathematical model that can transform project management elements into a unified value scale, providing a quantitative decision basis for differentiated resource allocation. The formula for the data-driven decision value model is as follows: in, The data decision value score is a dimensionless normalized value and serves as the direct basis for subsequent resource allocation decisions. This represents the critical path impact factor, a dimensionless value, when the task is located on the critical path. When the task is non-critical, its value is determined by the formula. Calculations show that here This represents the total float time of tasks obtained from the project management software, while It is a reference time constant (e.g., (sky), used to... Dimensionless processing is performed to ensure the consistency of dimensions in addition operations in the denominator; It represents the urgency of the data type and is a dimensionless parameter. Its value is defined in a preset lookup table based on the business nature of the data content (such as personnel safety alarms or routine inspection photos). This represents the dependency of downstream processes, a dimensionless value used to measure the ability of the current process to unlock subsequent processes. Its value can be obtained by calculating the number of immediate successor processes and then normalizing it. To more accurately reflect the dependency relationship, a min-max normalization method is used here. First, the number of immediate successor processes for each process is obtained, denoted as . Then, iterate through all the processes in the entire project to obtain the maximum number of immediately succeeding processes. and minimum number of successor processes Dependence on downstream processes The calculation formula is as follows: when equal At that time, all processes All are set to 0.5. This method linearly maps the dependency of all processes to... The interval is such that the process with the highest dependency (unlocking the most subsequent tasks) is... The value is 1, and the lowest dependency is 0. The calculation method is clear and stable.

[0024] These represent weighting coefficients, which are dimensionless parameters, reflecting the relative importance of the critical path, data type, and process dependency, respectively, and satisfying the following conditions: Its value is dynamically updated periodically by the cost-benefit optimization module; This parameter represents the time difference between the time the data was generated and the time of evaluation, and is expressed in seconds (s). This represents the time decay constant, used to control the rate at which the value of information decays over time, and is measured in units of 1000 kJ / m². To ensure the exponential term It is dimensionless; When the system is running, the data decision value assessment module receives the data packet and immediately executes the above formula to calculate; this quantification process transforms the macro logic of project management into a value judgment of micro data, laying a mathematical foundation for realizing a value-driven resource allocation strategy.

[0025] Example 3 Please see Figure 4 The allocation process of the dynamic resource allocation module is as follows: The data decision value score is compared and analyzed with preset high-value thresholds and low-value thresholds; If the data decision value score is greater than or equal to the high value threshold, the data packet will be allocated to the high reliability channel. If the data decision value score is less than the high value threshold but greater than or equal to the low value threshold, the data packet will be allocated to the standard commercial channel. If the data decision value score is less than the low value threshold, the data packet will be allocated to a low-cost IoT channel or enter a delayed transmission queue. The process for setting a high-value threshold is as follows: The cumulative distribution function of data decision value scores in historical statistical data is used; a high-value threshold is set at a preset quantile, and the total amount of data above this quantile is matched with the secure bandwidth of the high-reliability channel. The process for setting a low-value threshold is as follows: Based on the cost preference coefficient set by the system administrator, determine the proportion of data to be sent late; maintain the probability density function of the data decision value score in real time, and set the low value threshold to a position where the proportion of data with a value lower than its value in the total data volume is equal to the proportion of data to be sent late.

[0026] In another preferred embodiment of the present invention, the dynamic resource allocation module receives a message carrying... The data packets are scored, and the transmission channel is assigned according to the threshold comparison rules; the dynamic resource allocation module will... Score and the system's preset high-value threshold and low value threshold Perform a comparison; if Data packets are assigned to a high-reliability channel; if Data packets are allocated to standard commercial channels; if Data packets are then allocated to low-cost IoT channels or placed in a delayed transmission queue; To ensure the feasibility and effectiveness of this allocation logic, the process of setting the two thresholds is coupled with system resources and business objectives; High value threshold The configuration method aims to achieve optimal resource matching; the system continuously collects historical statistics. Score and generate cumulative distribution function; The value is set at a specific quantile of the distribution function. The selection criteria for this quantile are: the expected total amount of historical data with a value score higher than this point matches the available secure bandwidth of the high-reliability channel; this method ensures the transmission resources of high-value data streams. Low value threshold The setup process translates high-level management strategies into technical parameters; this process is based on the cost preference coefficient set by the system administrator. Through functional relationships (e.g., Determine the proportion of the total data volume that can be processed at low cost. To clarify the functional relationship, the system will use the cost preference coefficient set by the administrator. Defined as a The values ​​are within a range, where 0 represents no cost considerations (performance is the priority), and 1 represents cost considerations to the greatest extent possible. The system also presets a 'maximum allowable latency ratio'. (For example, 0.3, meaning up to 30% of the data can be delayed). The specific value is determined by the following linear function: In this way, administrator preferences can be directly translated into executable technical parameters for the system through a clear and unambiguous linear relationship, avoiding uncertainty. The system also maintains all... The probability density function of the score ; The final value is set to satisfy the integral equation. The point; when the manager sets the cost preference coefficient At higher levels, As it rises, it prompts Upward adjustment allocates more data to low-cost processing; conversely, when At lower levels, This will decrease accordingly, ensuring that more data receives high-quality transmission; this mechanism transforms management preferences into precise, dynamic, and actionable control thresholds.

[0027] Example 4 The dynamic adjustment process of the cost-benefit optimization module is as follows: A system status monitoring module is introduced to measure the actual total communication cost and the weighted average actual data packet loss rate within an evaluation cycle. The actual total communication cost is compared with the baseline communication cost to obtain the cost item. The weighted average actual data packet loss rate is compared with the target data packet loss rate upper limit set by the administrator. If the weighted average actual data packet loss rate exceeds the target data packet loss rate upper limit, the difference between the two is squared to obtain the performance penalty item. If it does not exceed the target data packet loss rate upper limit, the performance penalty item is set to zero. Based on the preset cost preference coefficient and performance preference coefficient, the cost item and performance penalty item are weighted and combined to calculate the cost-benefit objective function value. The cost-benefit optimization module is further used for: The optimal weight coefficients are obtained by minimizing the cost-benefit objective function. These optimal weight coefficients are then sent to the data decision value assessment module to update the preset weight coefficients corresponding to the critical path impact factor, data type urgency, and downstream process dependence, thus achieving dynamic adjustment.

[0028] In an embodiment of the present invention, the cost-benefit optimization module realizes the adaptive adjustment function of the system; its dynamic adjustment process depends on the actual total communication cost and the weighted average actual data packet loss rate measured by the system status monitoring module within an evaluation period; After acquiring monitoring data, the cost-benefit optimization module performs adjustment calculations. It compares the actual total communication cost with the baseline communication cost to obtain a normalized cost item. Simultaneously, it compares the weighted average actual packet loss rate with the upper limit of the target packet loss rate set by the administrator. This comparison process involves calculating the square of the difference between the two to generate a performance penalty item only when the measured packet loss rate exceeds the target upper limit; otherwise, the performance penalty item is zero. Finally, based on preset cost preference coefficients and performance preference coefficients, the module performs a weighted combination of the cost item and the performance penalty item to calculate the cost-benefit objective function value representing the comprehensive cost of the current system operating state. The cost-benefit objective function formula is as follows: in, The objective function to be minimized is the dimensionless comprehensive cost-benefit value; Weight coefficient vector , are the solution variables for this optimization problem; The preference coefficient is a dimensionless coefficient set by the system administrator, satisfying... ; To configure in the current weight The actual total communication cost in the next evaluation period; The baseline communication cost is used to normalize cost items; To configure in the current weight The weighted average actual packet loss rate measured below; The upper limit of the target packet loss rate set for the administrator; The function ensures that the penalty is activated only when the packet loss rate exceeds the limit; The core function of the cost-benefit optimization module is to minimize the value of the cost-benefit objective function. As an optimization problem, the optimal solution is obtained by inverse optimization using an optimization algorithm. Minimize the optimal weight coefficient vector This optimization problem is specifically solved using gradient-based numerical optimization algorithms, such as the Stochastic Gradient Descent (SGD) algorithm. After one evaluation cycle, the objective function is calculated. Regarding the current weight vector gradient The gradient indicates the direction in which the objective function value increases the fastest. To minimize the objective function, the weight vector is updated in the opposite direction of the gradient, according to the following update rule: in, This is the learning rate, a preset small positive number used to control the step size of each update, ensuring the stability and convergence of the algorithm. By periodically iterating this update process, the weight vector... It will gradually converge to the cost-benefit objective function. The optimal solution that achieves a local or global minimum The optimal weight coefficient vector is then sent to the data decision value assessment module to update the weight coefficients in its internal model. ; This mechanism establishes a feedback correction loop between the data-driven decision-making value assessment model and the cost-benefit optimization model; once the system status monitoring module reports that the packet loss rate exceeds the standard, the objective function... The penalty term in the algorithm will be increased, and the optimization algorithm will reduce the total value of the function. New weight coefficients that can better improve the value of key data will be calculated. After the new weights are applied, the system performance indicators will be brought back to the preset target range in the next evaluation cycle, thereby achieving adaptive optimization of the system. The technical solution provided by this invention can effectively reduce communication costs, while ensuring the stability of key information transmission and improving the efficiency of project collaboration.

[0029] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A photovoltaic project construction information management system based on the Industrial Internet, characterized in that, include: The data acquisition module is used to capture raw data packets from the construction site; The data decision value assessment module is used to calculate the decision value of the raw data packets captured by the data acquisition module and obtain the data decision value score. The dynamic resource allocation module is used to allocate data packets to preset transmission channels and generate transmission decisions based on the data decision value score calculated by the data decision value assessment module. The cost-benefit optimization module dynamically adjusts the decision value calculation in response to the actual communication cost and actual data packet loss rate resulting from transmission decisions, forming a closed-loop adaptive adjustment.

2. The photovoltaic project construction information management system based on the Industrial Internet according to claim 1, characterized in that, The process of calculating decision value is as follows: Obtain the critical path impact factor, data type urgency, and downstream process dependency of the construction task associated with the data packet; weight the critical path impact factor, data type urgency, and downstream process dependency according to the preset weight coefficients, and perform attenuation calculation by combining the data timeliness parameter calculated from the data packet generation time to obtain the data decision value score.

3. The photovoltaic project construction information management system based on the Industrial Internet according to claim 2, characterized in that, The process for obtaining the critical path impact factor is as follows: Determine whether the construction task associated with the data packet is located on the project critical path preset by the project management software; If the construction task is located on the critical path of the project, the critical path impact factor is set to the preset maximum impact value. If the construction task is a non-critical task, the critical path impact factor is calculated using a normalized inverse function based on the total float time of the construction task obtained from the project management software.

4. The photovoltaic project construction information management system based on the Industrial Internet according to claim 1, characterized in that, The allocation process of the dynamic resource allocation module is as follows: The data decision value score is compared and analyzed with preset high-value thresholds and low-value thresholds; If the data decision value score is greater than or equal to the high value threshold, the data packet will be allocated to the high reliability channel. If the data decision value score is less than the high value threshold but greater than or equal to the low value threshold, the data packet will be allocated to the standard commercial channel. If the data decision value score is less than the low value threshold, the data packet will be allocated to a low-cost IoT channel or placed in a delayed transmission queue.

5. The photovoltaic project construction information management system based on the Industrial Internet according to claim 4, characterized in that, The process for setting a high-value threshold is as follows: The cumulative distribution function of data decision value scores in historical statistical data is used; a high-value threshold is set at a preset quantile, and the total amount of data above this quantile is matched with the secure bandwidth of the high-reliability channel.

6. The photovoltaic project construction information management system based on the Industrial Internet according to claim 4, characterized in that, The process for setting a low-value threshold is as follows: Based on the cost preference coefficient set by the system administrator, determine the proportion of data to be sent late; maintain the probability density function of the data decision value score in real time, and set the low value threshold to a position where the proportion of data with a value lower than its value in the total data volume is equal to the proportion of data to be sent late.

7. The photovoltaic project construction information management system based on the Industrial Internet according to claim 1, characterized in that, The dynamic adjustment process of the cost-benefit optimization module is as follows: A system status monitoring module is introduced to measure the actual total communication cost and the weighted average actual data packet loss rate within an evaluation period; the actual total communication cost is compared with the baseline communication cost to obtain the cost item; The weighted average actual packet loss rate is compared with the target packet loss rate limit set by the administrator. If the weighted average actual packet loss rate exceeds the target packet loss rate limit, the difference between the two is squared to obtain a performance penalty term. If it does not exceed the limit, the performance penalty term is set to zero. Based on the preset cost preference coefficient and performance preference coefficient, the cost items and performance penalty items are weighted and combined to calculate the cost-benefit objective function value.

8. A photovoltaic project construction information management system based on the Industrial Internet, as described in claim 2 or 7, characterized in that, The cost-benefit optimization module is further used for: The optimal weight coefficients are obtained by minimizing the cost-benefit objective function. These optimal weight coefficients are then sent to the data decision value assessment module to update the preset weight coefficients corresponding to the critical path impact factor, data type urgency, and downstream process dependence, thus achieving dynamic adjustment.

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