Photovoltaic project construction information management system based on industrial internet

Abstract

The present application relates to the technical field of data processing and project management, and particularly relates to a photovoltaic project construction information management system based on an industrial internet, comprising a data acquisition module for capturing original data packets from a construction site; a data decision value evaluation module for calculating decision values of the original data packets captured by the data acquisition module to obtain data decision value scores; a dynamic resource allocation module for allocating the data packets to preset transmission channels according to the data decision value scores calculated by the data decision value evaluation module to generate transmission decisions; and a cost-benefit optimization module for dynamically adjusting the decision value calculation in response to actual communication costs and actual data packet loss rates generated by the transmission decisions to form a closed-loop adaptive adjustment. The present application accurately supplies limited high-reliability network resources to key data that has a decisive effect on project targets, thereby effectively controlling the total communication cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and project management, and particularly relates to a photovoltaic project construction information management system based on an industrial internet. BACKGROUND

[0002] The construction of large distributed photovoltaic power stations has the characteristics of scattered operation points, complex environment, multiple crosswork types, and long construction period. During the construction process, a large amount of heterogeneous data, such as unmanned aerial vehicle inspection videos, Internet of Things sensor data, construction personnel state information, material warehouse entry and exit records, and key process acceptance reports, need to be transmitted in real time or quasi-real time between each scattered construction point and the central command center.

[0003] The prior art usually adopts two extreme strategies when dealing with such problems: one is a high-reliability fixed network strategy, which pursues an extremely low data packet loss rate by deploying optical fibers or high-cost facilities such as comprehensive 5G / satellite communication, but the economic efficiency is poor; the other is a low-cost best-effort strategy, which uses public wireless networks or unlicensed frequency band technologies, and cannot guarantee data transmission stability when the network is congested, resulting in delays or losses of critical construction information and causing invalid waiting between processes, which seriously affects the project progress.

[0004] Therefore, the prior art generally has an unsolved technical contradiction: how to significantly reduce the construction and operation costs of IT infrastructure while ensuring the effective transmission of key data and stabilizing the data packet loss rate within an acceptable range. SUMMARY

[0005] The purpose of the present application is to provide a photovoltaic project construction information management system based on an industrial internet, which solves the problems in the background art.

[0006] To solve the above technical problems, the present application provides a photovoltaic project construction information management system based on an industrial internet, comprising:

[0007] The data acquisition module is used to capture raw data packets from the construction site;

[0008] The data decision value evaluation module is used to calculate the decision value of the raw data packets captured by the data acquisition module to obtain a data decision value score;

[0009] The dynamic resource allocation module is used to allocate the data packets to the preset transmission channel according to the data decision value score calculated by the data decision value evaluation module to generate a transmission decision;

[0010] The cost-benefit optimization module is used to dynamically adjust the decision value calculation in response to the actual communication cost and actual data packet loss rate generated by the transmission decision to form a closed-loop adaptive adjustment.

[0011] Preferably, the process of decision value calculation is as follows:

[0012] Obtain the critical path impact factor of the construction task associated with the data packet, the data type urgency, and the downstream process dependency; weight the critical path impact factor, the data type urgency, and the downstream process dependency according to the preset weight coefficient, and combine the data timeliness parameter calculated at the time when the data packet is generated to perform decay calculation, to obtain a data decision value score.

[0013] Preferably, the process of obtaining the critical path impact factor is as follows:

[0014] Determine whether the construction task associated with the data packet is located on the project critical path preset by the project management software;

[0015] If the construction task is located on the project critical path, the critical path impact factor is set to the preset maximum impact value;

[0016] If the construction task is a non-critical task, the critical path impact factor is calculated by a normalization inverse function according to the total float time of the construction task obtained from the project management software.

[0017] Preferably, the allocation process of the dynamic resource allocation module is as follows:

[0018] Compare and analyze the data decision value score with the preset high value threshold and low value threshold;

[0019] If the data decision value score is greater than or equal to the high value threshold, the data packet is allocated to a high reliability channel;

[0020] If the data decision value score is less than the high value threshold and greater than or equal to the low value threshold, the data packet is allocated to a standard commercial channel;

[0021] If the data decision value score is less than the low value threshold, the data packet is allocated to a low-cost Internet of Things channel or enters a delay sending queue.

[0022] Preferably, the process of setting the high value threshold is as follows:

[0023] Calculate the cumulative distribution function of the data decision value score in the historical data; set the high value threshold at a preset quantile point, and match the total amount of data higher than the quantile point with the safety bandwidth of the high reliability channel.

[0024] Preferably, the process of setting the low value threshold is as follows:

[0025] According to the cost preference coefficient set by the system administrator, the proportion of the delayed data is determined; the probability density function of the data decision value score is maintained in real time, and the low value threshold is set to the position where the proportion of the data with a value lower than the threshold in the total data amount is equal to the proportion of the delayed data.

[0026] Preferably, the dynamic adjustment process of the cost-benefit optimization module is as follows:

[0027] The system state monitoring module is introduced to measure the actual total communication cost and the weighted average actual packet loss rate in an evaluation period; the actual total communication cost is compared with the benchmark communication cost to obtain the cost term; the weighted average actual packet loss rate is compared with the target packet loss rate upper limit set by the administrator, and if the weighted average actual packet loss rate exceeds the target packet loss rate upper limit, the difference between the two is squared to obtain the performance penalty term, and if the weighted average actual packet loss rate does not exceed the target packet loss rate upper limit, the performance penalty term is set to zero; according to the pre-set cost preference coefficient and performance preference coefficient, the cost term and the performance penalty term are weighted and combined to calculate the cost-benefit target function value.

[0028] Preferably, the cost-benefit optimization module is further used for:

[0029] By minimizing the cost-benefit target function value, the optimal weight coefficient is obtained; and the optimal weight coefficient is sent to the data decision value evaluation module to update the pre-set weight coefficients corresponding to the critical path influence factor, the data type urgency and the downstream process dependency, so as to realize dynamic adjustment.

[0030] Compared with the prior art, the present application has the following beneficial effects:

[0031] (1) In a specific application scenario, assuming that a pouring completion data packet associated with a pile pouring task on the project critical path is generated, the dynamic resource allocation module will immediately allocate it to the high-reliability satellite communication channel for transmission, and the module will allocate it to the low-cost Internet of Things channel or put it into the delayed sending queue. The present scheme avoids paying expensive satellite channel fees for this regular inspection photo, and also avoids losing critical pouring completion information due to network congestion, thereby reducing unnecessary data transmission costs to the minimum on the premise of ensuring the project to proceed as planned, and achieving a substantial reduction in IT total cost.

[0032] (2) Improve project management and collaborative work efficiency, eliminate the waste of working hours caused by poor information, when the high-value data package of pouring is completed is reliably and timely transmitted to the central command center, the project management system can instantly trigger the next process, through the accurate identification of information value by the data decision value evaluation module and the reliable transmission guarantee of the subsequent process, to ensure the seamless connection between processes, and the smooth flow of information is directly translated into the protection of project progress and the saving of cost.

[0033] (3) Give the system the ability to adapt from passive response to active optimization, achieve dynamic optimal matching of project goals and IT resources, so that the system is no longer a static rule executor, but an intelligent system that can actively learn and adapt to project dynamic changes, and continuously seek the best balance point of cost and performance. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 is the logic diagram of the system of the present application;

[0036] Figure 2 is the logic diagram of the decision value calculation of the present application;

[0037] Figure 3 is the logic diagram of the key path influence factor acquisition of the present application;

[0038] Figure 4 is the logic diagram of the dynamic resource allocation module of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0040] Embodiment one

[0041] Please refer to Figure 1 The present application provides a photovoltaic project construction information management system based on industrial internet, comprising:

[0042] The data acquisition module is used to capture the original data package from the construction site;

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] Example 2

[0048] Please see Figure 2 The process of calculating decision value is as follows:

[0049] 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.

[0050] Please see Figure 3 The process for obtaining the critical path impact factor is as follows:

[0051] determining whether the construction task associated with the data packet is located on the project critical path preset by the project management software;

[0052] if the construction task is located on the project critical path, the critical path influence factor is set as a preset maximum influence value;

[0053] if the construction task is a non-critical task, the critical path influence factor is calculated by a normalization inverse function according to the total float time of the construction task obtained from the project management software.

[0054] In a preferred embodiment of the present application, the data decision value evaluation module calculates the data decision value score of each data packet through a multi-dimensional quantitative evaluation model; the technical implementation path of the module for performing decision value calculation lies in that it obtains three core value dimensions: first, the critical path influence factor, which is determined by analyzing the construction task associated with the data packet content from the project management software and judging whether it is located on the project critical path; second, the data type urgency, which is matched from the system built-in configurable query table according to the business attribute of the data itself;

[0055] Specifically, the query table assigns a non-negative 'basic priority score' ( ) to each preset data type (such as personnel safety alarm, key process acceptance report, routine inspection photo, etc.). The system traverses all preset data types to obtain the highest priority score as the'maximum priority score' ( ) and the lowest priority score as the'minimum priority score' ( ). The data type urgency is calculated by the minimum-maximum normalization of the basic priority score, ensuring that its value is accurately mapped to the interval.

[0056] To ensure the implementability of the technical solution, the calculation formula of the data type urgency is as follows:

[0057]

[0058] When is equal to , the of all data types is set to 0 or 0.5, for example, the of 'personnel safety alarm' can be set to 100, the of 'key process acceptance report' can be set to 70, and the of 'routine inspection photo' can be set to 20. At this time, is 100, is 20. Then the The calculation of the safety warning, the acceptance report and the routine inspection photo are as follows: This way provides strict normalization, clear quantifiable emergency evaluation standards for data of different business attributes.

[0059] Thirdly, the degree of dependence on downstream processes is quantified by analyzing the process logic relationship network in the project management software.

[0060] To ensure the feasibility of the technical solution, the acquisition process of the critical path impact factor is designed as a deterministic assignment logic. The data decision value evaluation module first judges the construction task. If the task is marked as on the critical path by the project management software, it is assigned a 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 through the inverse normalization function.

[0061] The data decision value evaluation module weights and sums the above three impact factors according to a set of dynamically adjustable weight coefficients, and introduces a timeliness parameter calculated based on the data packet timestamp to perform exponential decay calculation on the weighted result, and finally obtains the normalized data decision value score. The calculation process is defined by the data decision value model formula, which aims to create a mathematical model that can convert project management elements into a unified value scale, providing quantitative decision basis for differentiated resource allocation.

[0062] The data decision value model formula is as follows:

[0063]

[0064] Among them, represents the data decision value score, which is a dimensionless normalized value and is the direct basis for subsequent resource allocation decisions.

[0065] represents the critical path impact factor, which is a dimensionless value. When the task is on the critical path, When the task is a non-critical task, its value is calculated by the formula Here, represents the total float time of the task obtained from the project management software, and is a reference time constant (e.g. days), which is used to normalize to ensure the dimensional consistency of the addition operation in the denominator.

[0066] ​​Data type urgency, a dimensionless parameter, is defined in a preset query table according to the business nature of the data content (such as personnel safety alarm or routine inspection photo);

[0067] Downstream process dependency, a dimensionless value, is used to measure the unlocking ability of the current process to the subsequent process, and its value can be obtained by calculating the number of immediate subsequent processes of the process and performing normalization processing; in order to more accurately reflect the dependency relationship, the minimum-maximum normalization method is used here. First, the number of immediate subsequent processes of each process is obtained, denoted as . Then, all processes of the project are traversed to obtain the maximum number of immediate subsequent processes and the minimum number of immediate subsequent processes . The calculation formula of downstream process dependency is as follows:

[0068]

[0069] When is equal to , the of all processes is set to 0.5. This method linearly maps the dependency of all processes to the interval, so that the value of the process with the highest dependency (which unlocks the most subsequent tasks) is 1, and the value of the process with the lowest dependency is 0. The calculation method is clear and stable.

[0070] Weight coefficient, a dimensionless parameter, respectively represents the relative importance of the critical path, data type and process dependency, and satisfies , its value is dynamically updated periodically by the cost-benefit optimization module;

[0071] Data timeliness parameter, which refers to the time difference between the data generation time and the evaluation time, is in seconds (s);

[0072] Time decay constant is used to control the speed of information value decay over time, and its unit is , to ensure that the exponential term is dimensionless;

[0073] When the system is running, the data decision value evaluation module receives the data packet and immediately performs the above formula calculation; this quantization process converts the macro logic of project management into a value judgment of micro data, laying a mathematical foundation for the implementation of value-driven resource allocation strategy.

[0074] Example three

[0075] Please refer toFigure 4 The allocation process of the dynamic resource allocation module is as follows:

[0076] The data decision value score is compared with a preset high value threshold and a low value threshold.

[0077] If the data decision value score is greater than or equal to the high value threshold, the data packet is allocated to a high reliability channel.

[0078] If the data decision value score is less than the high value threshold and greater than or equal to the low value threshold, the data packet is allocated to a standard commercial channel.

[0079] If the data decision value score is less than the low value threshold, the data packet is allocated to a low-cost Internet of Things channel or enters a delay sending queue.

[0080] The setting process of the high value threshold is as follows:

[0081] The cumulative distribution function of the data decision value score in historical data is counted, and the high value threshold is set at a preset quantile point, and the total amount of data higher than the quantile point is matched with the safety bandwidth of the high reliability channel.

[0082] The setting process of the low value threshold is as follows:

[0083] According to the cost preference coefficient set by the system administrator, the proportion of data for delay sending is determined, the probability density function of the data decision value score is maintained in real time, and the low value threshold is set to a position that enables the proportion of data with a value lower than it in the total data amount to be equal to the proportion of data for delay sending.

[0084] In another preferred embodiment of the application, the dynamic resource allocation module receives a data packet carrying a score, and completes the assignment of a transmission channel through a threshold comparison rule; the dynamic resource allocation module compares the score with a system preset high value threshold and a low value threshold ; if , the data packet is allocated to a high reliability channel; if , the data packet is allocated to a standard commercial channel; if , the data packet is allocated to a low-cost Internet of Things channel or enters a delay sending queue.

[0085] To ensure the implementability and effectiveness of the allocation logic, the setting process of the two thresholds is coupled with system resources and business targets.

[0086] The setting method of the high value threshold aims to achieve optimal resource matching; the system continuously counts historical scores and generates a 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.

[0087] 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:

[0088]

[0089] 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.

[0090] Example 4

[0091] The dynamic adjustment process of the cost-benefit optimization module is as follows:

[0092] The system state monitoring module is introduced to measure the actual total communication cost and the weighted average actual packet loss rate in an evaluation period; the actual total communication cost is compared with the benchmark communication cost to obtain a cost term; the weighted average actual packet loss rate is compared with the upper limit of the target packet loss rate set by the manager, and if the weighted average actual packet loss rate exceeds the upper limit of the target packet loss rate, the square of the difference between the two is calculated to obtain a performance penalty term, and if the weighted average actual packet loss rate does not exceed the upper limit of the target packet loss rate, the performance penalty term is set to zero; the cost term and the performance penalty term are combined by weighting according to the preset cost preference coefficient and the performance preference coefficient to calculate the cost-benefit target function value;

[0093] The cost-benefit optimization module is further used for:

[0094] The optimal weight coefficient is obtained by minimizing the cost-benefit target function value, and the optimal weight coefficient is sent to the data decision value evaluation module to update the preset weight coefficients corresponding to the key path influence factor, the data type urgency and the downstream process dependency, so as to realize dynamic adjustment.

[0095] In the embodiment of the application, the cost-benefit optimization module realizes the adaptive adjustment function of the system; the dynamic adjustment process depends on the actual total communication cost and the weighted average actual packet loss rate measured by the system state monitoring module in an evaluation period;

[0096] After obtaining the monitoring data, the cost-benefit optimization module performs adjustment operation; it compares the actual total communication cost with the benchmark communication cost to obtain a normalized cost term; at the same time, it discriminates the weighted average actual packet loss rate from the upper limit of the target packet loss rate set by the manager; the discrimination process is that only when the measured packet loss rate exceeds the target upper limit, the square of the difference between the two is calculated to generate a performance penalty term; if it does not exceed, the performance penalty term is zero; finally, the module combines the cost term and the performance penalty term by weighting according to the preset cost preference coefficient and the performance preference coefficient to calculate the cost-benefit target function value representing the comprehensive cost of the current system running state;

[0097] The cost-benefit target function formula is as follows:

[0098]

[0099] Among them, is the cost-benefit target function to be minimized, and is the dimensionless comprehensive value;

[0100] is the weight coefficient vector is the solution variable of the optimization problem;

[0101] The preference coefficient is a dimensionless coefficient set by the system administrator, satisfying... ;

[0102] To configure in the current weight The actual total communication cost in the next evaluation period;

[0103] The baseline communication cost is used to normalize cost items;

[0104] To configure in the current weight The weighted average actual packet loss rate measured below;

[0105] The upper limit of the target packet loss rate set for the administrator;

[0106] The function ensures that the penalty is activated only when the packet loss rate exceeds the limit;

[0107] 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:

[0108]

[0109] 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. ;

[0110] This mechanism establishes a feedback correction logical closed loop between the data decision value evaluation model and the cost benefit optimization model; once the system state monitoring module feedbacks that the packet loss rate is out of standard, the penalty term in the objective function will increase, and the optimization algorithm will solve new weight coefficients that can improve the key data value score; after the application of the new weight, the system performance index will be pulled back to the preset target range in the next evaluation period, thereby realizing the adaptive optimization of the system; the technical scheme provided by the application can effectively reduce the communication cost while guaranteeing the transmission stability of the key information and improving the efficiency of project collaborative work.

[0111] The above is only a preferred embodiment of the application, and does not limit the application in other forms, and any person skilled in the art can use the disclosed technical content to make changes or modifications into equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made on the basis of the technical essence of the application to the above embodiments without departing from the technical scheme content of the application still belongs to the protection scope of the technical scheme of the application.

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 is used to dynamically adjust the decision value calculation in response to the actual communication cost and actual data packet loss rate generated by the transmission decision, forming a closed-loop adaptive adjustment. 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. 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.

2. 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.

3. The photovoltaic project construction information management system based on the Industrial Internet according to claim 2, 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.

4. The photovoltaic project construction information management system based on the Industrial Internet according to claim 2, 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.

5. 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.

6. A photovoltaic project construction information management system based on the Industrial Internet, as described in claim 1 or 5, 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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