Multi-source data fusion-based hazardous waste disposal facility matching and early warning system and method

Abstract

The invention discloses a hazardous waste disposal facility matching and early warning system and method based on multi-source data fusion in the technical field of hazardous waste disposal, and the system comprises a data collection module which is used for obtaining multi-source heterogeneous data; the data preprocessing module is used for analyzing, cleaning and standardizing the multi-source heterogeneous data to obtain standardized data; the intelligent analysis module is used for analyzing the standardized data to obtain a matching result or an early warning result; the intelligent analysis module comprises an intelligent matching sub-module which is used for calculating a comprehensive score of the hazardous waste disposal unit according to a multi-factor weighting algorithm and generating a matching result according to the comprehensive score. According to the system, data of multiple data sources are integrated through the data acquisition module, standardized data are obtained through the data preprocessing module, and the intelligent analysis module recommends disposal units for waste production enterprises based on a multi-factor weighting algorithm and the standardized data, gives out multi-level early warning and reasonably configures hazardous waste disposal resources.

Description

Technical Field

[0001] This invention relates to the field of hazardous waste treatment, and more specifically to a hazardous waste treatment facility matching and early warning system and method based on multi-source data fusion. Background Technology

[0002] The regularly published guidance announcements on the construction of hazardous waste utilization and disposal facilities and information on operating permits contain a large amount of key data on hazardous waste disposal. However, this key data is scattered across multiple government websites and suffers from problems such as inconsistent formats and untimely updates. At the same time, some existing hazardous waste management systems mainly focus on the digitalization of enterprise application processes or the monitoring of single paths, failing to effectively integrate multi-source and heterogeneous facility information and lacking macro-level resource optimization and allocation functions.

[0003] The aforementioned situation makes it difficult for waste-generating companies to quickly find suitable disposal units, and disposal units also struggle to effectively match clients, creating a contradictory situation where "clients cannot find disposal units" and "disposal units cannot find clients." This leads to some facilities operating beyond their capacity while others remain inefficiently idle, severely impacting the overall efficiency and resource allocation of the hazardous waste disposal industry. Therefore, there is an urgent need in the field of hazardous waste treatment for a system and method that can integrate multi-source data and achieve intelligent matching and scientific early warning. Summary of the Invention

[0004] The purpose of this invention is to provide a hazardous waste treatment facility matching and early warning system and method based on multi-source data fusion, which solves the problem that existing hazardous waste treatment systems lack multi-source data integration, hazardous waste treatment facility matching and early warning functions.

[0005] The present invention achieves the above objectives through the following technical solutions: A system and method for matching and early warning of hazardous waste disposal facilities based on multi-source data fusion, including: The data acquisition module is used to acquire multi-source heterogeneous data, which is data related to the hazardous waste disposal facilities of multiple hazardous waste disposal units; The data preprocessing module is used to parse, clean, and standardize the multi-source heterogeneous data to obtain standardized data; The intelligent analysis module is used to analyze the standardized data to obtain matching results or early warning results; The intelligent analysis module includes: The intelligent matching submodule is used to recommend appropriate disposal units to waste-generating enterprises based on a multi-factor weighted scoring algorithm and generate the matching results. The load warning submodule is used to generate the warning result based on the load rate; The comprehensive score S of the multi-factor weighted scoring algorithm is determined by the weighted sum of the following factors: a distance factor based on the geographical distance between the waste-generating enterprise and the disposal unit; a capacity matching factor based on the degree of matching between the code of the waste to be disposed of and the approved operating code of the disposal unit; a load status factor based on the current load status of the disposal unit; and a reputation factor based on the historical operating data of the disposal unit.

[0006] As a further optimization of the invention, the multi-factor weighting algorithm is as follows: ; in, For comprehensive scoring, , , and Distance factors Ability matching factor Load state factor and reputation factor The weighting coefficients.

[0007] As a further optimization of the invention, the distance factor The expression is: ; in, The attenuation coefficient is... ; Geographical distance refers to the straight-line distance or transportation route from the waste-generating enterprise to the hazardous waste disposal unit.

[0008] As a further optimization of the invention, the capability matching factor The matching score is obtained from comparing invalid codes. The invalid codes include industry code segments, sequence code segments, and category code segments. The comparison rules for the matching score are as follows: When the waste code of the hazardous waste to be treated matches the waste code of the hazardous waste disposal unit, the matching score is [scoring value missing]. ; When the hazardous waste to be treated only matches the corresponding code segment of the industry code segment and category code segment with the waste code of the hazardous waste disposal unit, the matching score is 1. ; When the category code segment of the hazardous waste to be treated matches the corresponding code segment of the waste code of the hazardous waste disposal unit, the matching score is [missing information]. ; When the category code segment of the hazardous waste to be treated does not match the corresponding code segment of the waste code of the hazardous waste disposal unit, the matching score is: ; in, .

[0009] As a further optimization of the invention, the load state factor The expression is as follows: ; ; in, For load factor, for The expression is sensitive to deviations from the ideal load rate. The parameters are determined by the vertex position. The parameter is the maximum value at the vertex. The cumulative amount of hazardous waste already disposed of, This refers to the annual approved disposal volume of hazardous waste disposal facilities.

[0010] As a further optimization of the invention, when the load factor At that time, the warning result was an idle warning; When the load factor At that time, the warning result is a normal operation indication; When the load factor At that time, the warning result was a saturation warning; When the load factor At that time, the warning result was an overload warning; in, .

[0011] As a further optimization of the invention, the data sources for the hazardous waste disposal facility-related data include publicly available government data, IoT data, and manually entered data, and the data acquisition module includes: The government data collection unit is used to automatically crawl publicly available government data related to hazardous waste using web crawling technology. The Internet of Things (IoT) data access unit is used to connect to the provincial solid waste environmental supervision information platform to obtain IoT data on the generation, storage, transfer and disposal of hazardous waste; The manual data entry interface unit is used to receive manually entered data.

[0012] As a further optimization of the invention, a data update management module is also included, which is used to control the data acquisition frequency of the data acquisition module and to check the data acquired by the data acquisition module.

[0013] As a further optimization of the invention, a data visualization module is also included, which is used to provide the matching results to waste-generating enterprises and the early warning results to regulatory authorities.

[0014] A method for matching and early warning of hazardous waste disposal facilities based on multi-source data fusion includes the following steps: Acquire multi-source heterogeneous data, which is data related to the hazardous waste disposal facilities of multiple hazardous waste disposal units; The multi-source heterogeneous data is parsed, cleaned, and standardized to obtain standardized data; Analyze the standardized data to obtain matching results or early warning results; In the process of obtaining matching results, a corresponding disposal unit is recommended for the waste-generating enterprise based on a multi-factor weighted scoring algorithm, thereby generating the matching results; During the process of obtaining the early warning results, the early warning results are generated based on the load rate; The comprehensive score S of the multi-factor weighted scoring algorithm is determined by the weighted sum of the following factors: a distance factor based on the geographical distance between the waste-generating enterprise and the disposal unit; a capacity matching factor based on the degree of matching between the code of the waste to be disposed of and the approved operating code of the disposal unit; a load status factor based on the current load status of the disposal unit; and a reputation factor based on the historical operating data of the disposal unit.

[0015] The beneficial effects of this invention are as follows: 1) This invention integrates publicly available government data, IoT platform data, and manually entered data through a data acquisition module, and cleans and standardizes the data through a data preprocessing module to build a unified facility information database. The intelligent analysis module uses a multi-factor weighted algorithm to recommend disposal units for waste-generating enterprises, which solves the long-standing problem of information dispersion and matching difficulties in the field of hazardous waste disposal, and improves facility utilization and resource allocation efficiency. 2) The intelligent matching algorithm of this invention comprehensively considers multiple key factors such as distance attenuation, capacity matching, load status and reputation evaluation, and performs weighted calculations through configurable weight coefficients, so that the final matching recommendation ranking not only considers geographical distance, but also takes into account the facility's processing capacity, real-time operating status and compliance reputation, thereby providing better and more reliable decision support for waste-generating enterprises. 3) This invention calculates facility load rates in real time and sets up multi-level early warnings to reflect the idle, normal, saturated, and overloaded status of hazardous waste disposal facilities. It can promptly identify areas and facility types with excess or insufficient capacity. At the same time, through the data visualization module, it provides regional heat maps and early warning reports to regulatory authorities, providing intuitive and powerful data support for macro-level capacity planning and control. Attached Figure Description

[0016] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a flowchart of the data acquisition module of the present invention; Figure 3 This is a flowchart of the preprocessing module of the present invention; Figure 4 This is a flowchart of the intelligent matching submodule of the present invention; Figure 5 This is a flowchart of the load early warning submodule of the present invention. Detailed Implementation

[0017] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0018] First Embodiment like Figure 1 As shown, this embodiment relates to a hazardous waste disposal facility matching and early warning system based on multi-source data fusion. This system includes a data acquisition module, a data preprocessing module, an intelligent analysis module, and a data visualization module. The data acquisition module is used to acquire multi-source heterogeneous data, which consists of data related to the hazardous waste disposal facilities of multiple hazardous waste disposal units. The data sources for the hazardous waste disposal facility-related data include publicly available government data, IoT data, and manually entered data. The data acquisition module includes a government data acquisition unit, an IoT data access unit, and a manual data entry interface unit.

[0019] Please see Figure 2 The government data collection unit automatically crawls publicly available government data related to hazardous waste using web crawling technology. This publicly available government data specifically includes information on the issuance of hazardous waste operation licenses and investment guidance announcements for the construction of hazardous waste utilization and disposal facilities published on the official websites of provincial and municipal environmental protection departments. The IoT data access unit connects to the provincial solid waste environmental supervision information platform via API to obtain IoT data on the generation, storage, transfer, and disposal of hazardous waste. The API is an application programming interface. The manual data entry interface unit receives manually entered data. This manually entered data is hazardous waste-related data that cannot be obtained through the aforementioned government data collection unit and IoT data access unit. This data collection module also includes a timed trigger, which periodically triggers the data collection module to acquire multi-source heterogeneous data. Initially, the data source is determined when acquiring multi-source heterogeneous data.

[0020] When the data source is publicly available government data, the government data collection unit is activated and data is obtained from the government website according to the following process: The government data collection unit starts a crawler task, parses the list of web pages, extracts the links to detail pages, crawls the data from the detail pages, downloads and parses the attachment content, and obtains publicly available government data. This publicly available government data is structured data. Afterwards, the government data collection unit writes the publicly available government data as raw data into the data temporary storage area.

[0021] When the data source is IoT data, the IoT data access unit is activated and IoT data is obtained according to the following process: The IoT data access unit configures API request parameters, sends an authentication request to the provincial solid waste environmental supervision information platform, obtains and calls the data interface of the provincial solid waste environmental supervision information platform, receives JSON / XML response, parses the data, and obtains IoT data related to hazardous waste. This IoT data is structured data and is written as raw data to the data temporary storage area.

[0022] When the data source is manually entered data, the manual data entry interface unit is activated, and the manually entered data is obtained from the manual data entry terminal according to the following process: The user of the manual data entry terminal logs into the management backend, selects to upload a file or fills in a form. After the user submits the manually entered data, the manual data entry interface unit performs system verification on the data format of the manually entered data. After successful verification, the manually entered data is written as the original data to the data temporary storage area.

[0023] like Figure 3 As shown, the data preprocessing module is used to parse, clean, and standardize the raw data to obtain standardized data. The preprocessing module includes a data parsing unit, a unit unification unit, a terminology standardization unit, and an address coding unit. The data preprocessing module receives the raw data from the data storage area, parses the content of the raw data to obtain parsed data, and then uses the unit unification unit to standardize the disposal capacity represented by different units in the parsed data to the standard unit "tons / year," for example, converting "10,000 tons / year" to "10,000 tons / year." The terminology standardization unit standardizes terms such as hazardous waste categories and treatment methods according to national standards; for example, converting "HW08 waste mineral oil, waste mineral oil HW08" to "HW08 waste mineral oil and mineral oil-containing waste." The geocoding unit converts text address information into latitude and longitude coordinates for subsequent spatial analysis and distance calculation. The geocoding unit achieves this function by calling the map API of an external map service provider.

[0024] Taking a waste-generating enterprise as an example, the geocoding unit submits the enterprise's address to the map API. The map API generates response data, and the geocoding unit parses the latitude and longitude coordinates (120.12, 30.16) from the map API's response data. These latitude and longitude coordinates are the address code, used to identify the geographical location of the enterprise. The standardized data is then output to the central database. After data parsing, unit standardization, and terminology standardization, the data, along with the address code, are all standardized data.

[0025] Please see Figure 4 and Figure 5The intelligent analysis module analyzes standardized data to obtain matching or early warning results. It includes an intelligent matching submodule and a load early warning submodule. When a user sends a matching result query request to the system, the intelligent matching submodule calculates a comprehensive score for each hazardous waste disposal unit using a multi-factor weighted algorithm and generates a matching result based on the comprehensive score. When a user sends an early warning result query request to the system, the load early warning submodule generates an early warning result based on the load rate.

[0026] like Figure 4 As shown, the multi-factor weighting algorithm is as follows: ; in, For comprehensive scoring, For distance factor, As a capability matching factor, For load state factors, As a reputation factor, , , and These are the weighting coefficients for the distance factor, capability matching factor, load status factor, and reputation factor, respectively.

[0027] The matching results are the top N hazardous waste disposal units in terms of overall score. .

[0028] Next, we will discuss the distance factor. Ability matching factor Load state factor and reputation factor The method of obtaining it will be explained as follows: 1) Distance factor ; Distance factor The expression is: ; in, This is the attenuation coefficient, used to control the sensitivity to the influence of distance. In this embodiment, The preferred value is 0.002, which means that the attenuation rate per kilometer is approximately 0.2%. Geographic distance, measured in kilometers, refers to the geographical distance between the location of the waste-generating enterprise and the location of the hazardous waste disposal facility. This distance is a straight-line distance calculated based on the geocoding of both addresses. In practical applications, this distance can be used as an effective estimate of the transportation route. Furthermore, for scenarios requiring precise routing distances, the actual transportation route can be obtained through the route planning API integrated with map services. This indicates that the waste-generating enterprise and the hazardous waste disposal facility are located at the same site. With geographical distance Gradually increase It gradually decreases and approaches 0.

[0029] 2) Ability matching factor ; Ability matching factor This is the matching score obtained from the waste code comparison. According to the "National Hazardous Waste List," waste codes include industry code segments, sequential code segments, and category code segments. A waste code is an 8-digit number in the format "XXX-XXX-XX," and it is unique. The first three digits of the waste code represent the industry code segment, determined according to the "National Industrial Classification of Economic Activities (GB / T 4754-2017)." The fourth to sixth digits represent the sequential code segment, and the seventh and eighth digits represent the category code segment. The matching score comparison rules are as follows: When the waste code of the hazardous waste to be treated matches the complete code segment of the waste code of the hazardous waste disposal unit, the matching score is 1. ; When the hazardous waste to be treated only matches the corresponding code segment of the industry code segment and category code segment with the waste code of the hazardous waste disposal unit, the matching score is 1. ; When the category code segment of the hazardous waste to be treated matches the corresponding code segment of the waste code of the hazardous waste disposal unit, the matching score is [missing information]. ; When the category code segment of the hazardous waste to be treated does not match the corresponding code segment of the waste code of the hazardous waste disposal unit, the matching score is: ; in, In this embodiment, The preferred value is 1.0. The preferred value is 0.9. The preferred value is 0.7. The preferred value is 0.

[0030] The matching rules are defined as follows: The waste code for the hazardous waste to be disposed of input by the waste-generating enterprise is A, and its format is "XXX-XXX-XX". The set of waste codes for hazardous waste disposal facilities approved for operation is as follows: The waste code format for hazardous waste disposal facilities is also "XXX-XXX-XX". Capacity matching factor. It is divided into four levels, and its calculation follows these rules: Rule 1, Exact Match: This means that the complete waste code of the waste to be disposed of is exactly the same as a certain waste code in the list of approved hazardous waste disposal facilities, and the first 8 digits of the waste code are exactly the same. .

[0031] Rule 2, Industry / Category Matching: Industry code segments and category code segments and sets of hazardous waste to be disposed of The industry code segment and category code segment of a certain piece of obsolete code are the same, for example, obsolete code The set is 321-004-48. A piece of junk code The code is 321-005-48. A comparison shows that the first three digits and the seventh and eighth digits are identical, meaning the industry and category code segments are the same, but the waste sequence code segment differs. Although the sources are slightly different, the waste properties and treatment processes are highly similar, indicating a high degree of matching and a high capacity matching factor. .

[0032] Rule 3, Category Matching: Category code segments and sets of hazardous waste to be disposed of The same code segment belongs to a certain category of waste code, for example, waste code The code is 321-004-48, set A piece of junk code The code is 323-001-48, with the 7th and 8th digits being the same, indicating the same category code segment, but different industry and sequence code segments. (This is related to the capability matching factor.) .

[0033] Rule 4, Mismatch (Score = 0): If none of the above conditions are met, it indicates that the facility is not qualified to dispose of this type of waste. Capability matching factor. .

[0034] Based on the above rules The function expression can be defined as: ; It should be noted that the conditions in the above formula are evaluated sequentially; if any condition is met, the corresponding score is returned and subsequent evaluations cease. When multiple conditions are met simultaneously, the highest score is taken, following the "highest principle." For set A piece of useless code in China Waste code Industry code segment and category code segment, Waste code Industry code segment and category code segment, Waste code Category code segment, Waste code The category code segment. For example, if a hazardous waste satisfies both "exact match" and "industry and category match", then the capability matching factor... .

[0035] Taking waste with waste code 251-002-08 as an example, this waste is oily sludge waste mineral oil, and the matching rules are shown in Table 1: Matching Level Matching Conditions Explanation Score C(cap) Example (Facility Approved Waste Code) Exact match, complete garbage code, exactly the same 1.0251-002-08 Industry / Category match The first three digits and the seventh and eighth digits of the code are the same, but the complete codes are different (the fourth to sixth digits are different, indicating that the waste source is the same, but the specific order and category are slightly different). The seventh and eighth digits of the category matching code 0.9251-005-08 are the same, but the industry and order codes of the waste are different (the first six digits are different). 0.7071-001-08 None of the above conditions are met. 263-009-04 Table 1 Matching Rules Table The aforementioned waste-generating enterprises produce waste with waste code 251-002-08 awaiting disposal. The matching level is calculated using the hazardous waste disposal facility matching and early warning system. Table 1 shows four hazardous waste disposal units, each equipped with one hazardous waste disposal facility. These facilities are categorized as Facility A, Facility B, Facility C, and Facility D. The analysis of these facilities is as follows: For facility A, its approved list includes waste code 251-002-08, which is identical to the complete waste code of the waste to be disposed of, thus indicating a perfect match. .

[0036] For Facility B, its approved list includes waste code 251-005-08, where the first three digits are 251 and the seventh and eighth digits are 08. Code 251-005-08 matches the waste code of the waste to be disposed of in terms of industry and category, but the sequence code is different, indicating an industry / category match. .

[0037] For facility C, its approved list includes waste code 071-001-08, with category code 08. Code 071-001-08 matches the waste code of the waste to be disposed of only in the category code, therefore it is determined to be a category match. .

[0038] For facility D, its approved list only contains waste code 263-009-04, which differs in category code from the waste code for the waste to be disposed of. This is considered a mismatch. .

[0039] 3) Load state factor ; This refers to the ratio of the cumulative hazardous waste treated by a hazardous waste disposal facility to its annual approved disposal capacity. To achieve accurate evaluation of the load status of hazardous waste disposal facilities and guide optimal capacity allocation, this embodiment constructs a load status factor function. . It is a function used to assess whether the load factor is "appropriate," that is, the current load factor. A normalized score is assigned, and the score does not change monotonically but rather shows a significant preference for a "optimal range" where the load rate is within a range that ensures both operational efficiency and appropriate redundancy. The aim is to prioritize facilities with moderate load rates to avoid overloading or idleness.

[0040] The data is obtained as follows: it is acquired from government guidance announcements or data reported by enterprises through the data collection module, and then calculated after the preprocessing module unifies the units.

[0041] Load state factor The expression is a quadratic function model in one variable, and the specific function expression is as follows: ; ; in, For load factor, , and These are three key parameters. for The sensitivity parameter of the expression to deviations from the ideal load rate determines the downward opening of the function graph and the sharpness of the curve. The parameters are determined by the vertex position. The score for a vertex is the maximum parameter at that vertex. The cumulative amount of hazardous waste already disposed of, This refers to the annual approved disposal volume of hazardous waste disposal facilities.

[0042] when At that time, the warning result was an idle warning; when At that time, the warning result is a normal operation indication; when At that time, the warning result was a saturation warning; when At that time, the warning result was an overload warning; in, In this embodiment, Preferably 30%, The preferred value is 70%. The optimal value is 90%. Considering that the operating efficiency of hazardous waste disposal facilities does not change linearly, the above-mentioned quadratic function model is used to calculate the load state factor in order to achieve the above-mentioned optimal range.

[0043] In this embodiment, based on the analysis and verification of operational data from the hazardous waste disposal industry, the vertex of the function is set at a load rate of 70%, i.e. This point is considered an ideal value that balances processing efficiency and safety redundancy. To ensure the function value is within the load factor... Given a vertex whose domain is [0, 1] and whose maximum value is normalized to 1, set the vertex score. To ensure the function's score significantly decreases when the load rate approaches 0% or 100%, thus serving a guiding role, the curve is designed to be smooth and reasonable, with a quadratic term coefficient... It was determined to be -4.

[0044] Therefore, the load state factor function The specific mathematical expression is defined as follows: , ; It should be noted that this function is in When the load factor reaches its maximum value of 1, the load rate is 70%; as the load rate deviates from 70%, the score gradually decreases. For example, when... or hour, ;when or hour, The function curve is continuous and smooth, and can accurately and quantitatively reflect the suitability of the load rate throughout the entire range from idle to overload.

[0045] Furthermore, considering the possibility that in actual operation, there may be situations where the approved capacity is full but waste is still being received, i.e. This function can be further defined as follows: when At that time, take This means fixing its score at the lowest level of the overload state to provide a strong negative guidance.

[0046] 4) Reputation Factor ; Reputation Factor It is a comprehensive quantitative evaluation of the compliance, operational stability, and service quality of disposal facilities. This parameter is a normalized score, represented as a value between 0 and 1, with 1 representing the highest credit rating. It is calculated based on publicly available authoritative data through a quantitative and transparent multi-indicator comprehensive evaluation system.

[0047] It can be obtained through a combination of calculations using the following multiple data sources: ① Official rating: Integrates the enterprise environmental credit evaluation results released by the ecological and environmental protection department, and maps the levels such as "excellent", "good", "qualified" and "poor" to numerical values ​​(such as 1.0, 0.8, 0.6, 0.3).

[0048] ② Violation Records: Points will be deducted based on the number and severity of publicly disclosed environmental administrative penalties.

[0049] ③ Operational stability: Bonus points are awarded based on the number of years the facility has been in continuous and stable operation.

[0050] ④ User Feedback: In the later stages of platform operation, service evaluations from waste-generating companies will be incorporated as a reference. This user feedback is an optional indicator and may or may not be included in the calculation of the reputation factor.

[0051] The expression directly uses the raw evaluation scores from the aforementioned data sources. The normalized result is obtained through a linear transformation. The expression is as follows: , ; in, For the reputation score pre-calculated and stored in a central database, The initial value is 100. The values ​​are adjusted periodically based on several indicators, including official ratings, violation records, operational stability, load rate stability, and user feedback. In this embodiment, Points will be deducted or added based on the following four categories of indicators. The score changes for each indicator are based on verifiable data within a specific time period (such as the most recent three calendar years).

[0052] Credit Score The calculation rules are as follows: ① Official environmental credit rating Scoring rules: Rated as "Excellent", "Green", or "Grade A": It remains unchanged.

[0053] Rating: "Good", "Blue", or "B" 5 points will be deducted.

[0054] The rating is "Pass", "Yellow", or "C". 15 points will be deducted.

[0055] The rating is "Poor", "Red", or "D". 40 points will be deducted.

[0056] Within the same evaluation period, the lowest score will be used, and deductions will not be made repeatedly.

[0057] Official environmental credit ratings are used to demonstrate the compliance of hazardous waste disposal companies.

[0058] ② Environmental administrative penalty records The data comes from administrative penalty decisions published on the official website of the ecological and environmental protection department. Each instance of a general administrative penalty (such as due to improper record-keeping) is considered a violation. 10 points will be deducted; for each major administrative penalty (such as environmental pollution or social impact caused by illegal transfer, dumping, or excessive emissions): 30 points will be deducted.

[0059] Environmental administrative penalty records are used to reflect the operational compliance of hazardous waste disposal units.

[0060] ③ Operational stability The data is derived from the initial issuance date of the enterprise's hazardous waste management license. For each full year of continuous and stable operation (without major administrative penalties) from the date of initial license issuance, [further details are needed]. Add 2 points. This is a bonus point item designed to encourage continued compliant operations, but there is a cap; the maximum bonus points can not exceed 20.

[0061] Operational stability is used to reflect the reliability of a hazardous waste disposal unit.

[0062] ④ Facility load rate stability The data comes from historical load factor data collected by the system of this invention. The standard deviation of the monthly load factor over the past year is calculated. .like This indicates that the load factor is very stable. Add 5 points; if This indicates that the load factor is basically stable. Add 2 points; if This indicates that the load factor fluctuates significantly. 5 points will be deducted.

[0063] The stability of facility load rate is used to reflect the management level of hazardous waste disposal units.

[0064] After calculation according to the above rules, for Implement capping and minimum guarantee measures: If ,but ;like ,but Ultimately, the reputation factor The formula is obtained through linear normalization, as follows: All data collection, calculation, and updates are performed automatically by the system every six months to ensure the timeliness of the evaluation.

[0065] The data visualization module is used to display matching results or alert results. The data visualization module includes: The waste-generating enterprise service interface generation unit is used to provide matching results for waste-generating enterprises; The enterprise service interface generation unit is used to provide disposal enterprises with a list of enterprises that produce hazardous waste to be treated, the waste codes of the hazardous waste to be treated, and system suggestions; The regulatory analysis interface generation unit is used to provide early warning results to regulatory authorities. The public query interface generation unit is used to display the load rate of hazardous waste treatment facilities, hazardous waste treatment methods, and various parameters of the facility's operation, providing the public with basic information query and supervision functions.

[0066] like Figure 5 As shown, the load early warning submodule includes a load factor calculation unit, a multi-level early warning unit, and a regional capacity analysis unit. The load factor calculation unit is used to calculate the real-time load factor of each facility. The multi-level early warning unit is used to set up a four-level early warning mechanism. The regional capacity analysis unit is used to calculate the load rate and load distribution of various hazardous waste disposal facilities in the region. The load distribution is displayed through a regional heat map.

[0067] After receiving the approved operating scale and accumulated processed volume, the load early warning submodule calculates the load rate of each facility through the load rate calculation unit. The multi-level early warning unit judges the load rate range, analyzes the load rate range of each facility, and generates and outputs early warning results based on the load rate range. The regional capacity analysis unit generates facility-level early warning output and regional early warning output based on the early warning results. The facility-level early warning output and regional early warning output are displayed through the visualization module.

[0068] The facility-level early warning output is a list of facilities, displaying the load rates of different hazardous waste disposal facilities and generating icons with colors matching the load rate ranges. The region-level early warning output is a regional heat map, where the colors of the areas where different hazardous waste disposal facilities are located match the load rate ranges of the facilities. Blue areas indicate areas with overcapacity, and red areas indicate areas with insufficient capacity. Additionally, the regional capacity analysis unit is used to generate early warning reports when the load rate of hazardous waste disposal facilities consistently exceeds a set value, and sends these reports to regulatory authorities. The set value is 85%, and the early warning report indicates insufficient capacity in the region where the facility is located.

[0069] The early warning mechanism is as follows: When the load factor range is When a blue alert is issued, indicating an idle hazardous waste disposal facility, it signifies that the facility is not in use. This blue alert is displayed through a data visualization module, which adjusts the color of the area corresponding to that load rate range to blue. The data visualization module's service interface for disposal companies provides system suggestions for expanding market reach.

[0070] when When the hazardous waste disposal facility is operating normally, a green "normal" alert is issued. This alert is displayed through a data visualization module, and the area corresponding to that load rate range is adjusted to green.

[0071] When the load factor range is When a yellow alert is issued, indicating a saturation warning, it signifies that the hazardous waste disposal facility is approaching saturation operation. This yellow alert is displayed through a data visualization module, adjusting the color of the area corresponding to the load rate range to yellow. The data visualization module's service interfaces for both disposal and waste-generating companies provide this indication of near-saturation operation.

[0072] When the load factor range is When the hazardous waste disposal facility reaches its approved operating capacity, a red alert (overload warning) is issued, indicating that the facility is approaching its approved operating capacity and is about to operate beyond its capacity. This red alert is displayed through a data visualization module, which adjusts the color of the area corresponding to the load rate range to red. The data visualization module's service interfaces for both disposal and waste-generating companies display a message indicating that accepting new customers is temporarily suspended.

[0073] The hazardous waste disposal facility matching and early warning system also includes a data update management module, which comprises an automatic update scheduler, a data quality checker, and a version management unit. The automatic update scheduler controls the data acquisition module at a preset frequency, enabling it to automatically execute data acquisition tasks; the data quality checker performs integrity, accuracy, and consistency checks on the data acquired by the data acquisition module; and the version management unit records data change history and version information.

[0074] The hazardous waste disposal facility matching and early warning system adopts a distributed architecture, comprising a web server, application server, database server, and data acquisition server. The data acquisition module is deployed on a dedicated data acquisition server, running a Python environment and the Scrapy framework, responsible for executing web crawling and API call tasks. The intelligent analysis module, data preprocessing module, and data update management module are deployed on a Tomcat server, which handles core business logic calculations. All standardized data generated by the system is stored in a MySQL cluster on the database server, and frequently accessed data is cached using a Redis database to improve performance. Finally, the data visualization module provides interactive interfaces and data display services to various users through an Nginx server. The Scrapy framework is a web crawling framework, Tomcat is an application server, the MySQL cluster is a multi-machine collaborative database architecture, the Nginx server is a web server, and the Redis database is a caching database.

[0075] The data visualization module is built and deployed using a B / S architecture. Its front-end interface is developed using the Vue.js framework and integrates the ECharts visualization library to render various data charts. The front-end design employs responsive web design technology, automatically adapting to different screen sizes and supporting access and operation on both PCs and mobile devices. The B / S architecture is a browser / server architecture, a software system architecture where users access the application through a browser, and business logic is processed on the server side. Vue.js is a progressive programming framework for building user interfaces. ECharts is an open-source visualization library based on the Java language.

[0076] The data visualization module offers differentiated customized interfaces to meet the functional needs of different user roles. Specifically, the waste-generating enterprise service interface provides waste-generating enterprises with functions such as facility search, intelligent matching and recommendation, and contact and coordination. The regulatory analysis interface provides regulatory users such as environmental protection departments with regional heat map displays and early warning reports. The public query interface provides the public with functions to query basic information on hazardous waste disposal facilities and monitor their operational status.

[0077] Second Embodiment This embodiment relates to a method for matching and early warning of hazardous waste disposal facilities based on multi-source data fusion. This method is applicable to the hazardous waste disposal facility matching and early warning system of the previous embodiment, and includes the following steps: Step S1: Obtain multi-source heterogeneous data from multiple data sources through the data acquisition module. The multi-source heterogeneous data consists of data related to the hazardous waste disposal facilities of multiple hazardous waste disposal units. Step S2: The multi-source heterogeneous data is parsed, cleaned and standardized through the data preprocessing module to obtain standardized data, and the standardized data is output to the central database. Step S3: The user inputs a query request into the system, and the intelligent analysis module analyzes standardized data based on the user's request. When the query request is a matching result query request from a waste-generating enterprise user, the intelligent analysis module generates matching results through the intelligent matching submodule; when the query request is an early warning result query request from a regulatory department or hazardous waste disposal unit, the intelligent analysis module generates early warning results through the load early warning submodule. Step S4: Display the matching results or warning results through the data visualization module; In step S3, during the process of obtaining matching results, the intelligent matching submodule calculates the comprehensive score of each hazardous waste disposal unit according to the multi-factor weighted algorithm, and generates matching results based on the comprehensive score.

[0078] The multi-factor weighting algorithm is as follows: ; in, For comprehensive scoring, For distance factor, As a capability matching factor, For load state factors, As a reputation factor, , , and These are the weighting coefficients for the distance factor, capability matching factor, load status factor, and reputation factor, respectively.

[0079] During the process of obtaining early warning results, early warning results are generated based on the load rate.

[0080] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A hazardous waste disposal facility matching and early warning system based on multi-source data fusion, characterized in that, The method comprises the following steps: a data acquisition module for acquiring multi-source heterogeneous data, wherein the multi-source heterogeneous data is hazardous waste disposal facility related data of multiple hazardous waste disposal units; a data preprocessing module for analyzing, cleaning and standardizing the multi-source heterogeneous data to obtain standardized data; an intelligent analysis module for analyzing the standardized data to obtain a matching result or a warning result; the intelligent analysis module comprises: an intelligent matching sub-module for recommending a corresponding disposal unit to a waste-producing enterprise based on a multi-factor weighted scoring algorithm to generate the matching result; a load warning sub-module for generating the warning result according to a load rate, wherein the load rate is a ratio of a cumulative disposed amount of hazardous waste to an annual approved disposal amount of a hazardous waste disposal facility; wherein the comprehensive score S of the multi-factor weighted scoring algorithm is determined by the following factors: a distance factor determined based on the geographical distance between the waste-producing enterprise and the disposal unit; an ability matching factor determined based on the matching degree of the waste code to be disposed and the approved operating code of the disposal unit; a load state factor determined based on the current load state of the disposal unit; and a credit factor determined based on the historical operation data of the disposal unit.

2. The hazardous waste facility matching and early warning system of claim 1, wherein: The multi-factor weighted algorithm is as follows: ; wherein, is a comprehensive score, , , and are weight coefficients of a distance factor , a capability matching factor , a load status factor and a reputation factor , respectively.

3. The hazardous waste treatment facility match and warning system of claim 1 wherein: The distance factor The expression for the distance factor is: ; wherein, is a decay coefficient, ; is a geographic distance, which is a straight-line distance or a distance of a transportation path from the hazardous waste producing enterprise to the hazardous waste disposal unit.

4. The hazardous waste treatment facility match and warning system of claim 1 wherein: The ability matching factor For the waste code matching result matching score, the waste code includes an industry code segment, a sequential code segment, and a category code segment, and the matching score comparison rule is as follows: When the waste code of the hazardous waste to be treated is consistent with all code segments of the waste code of the hazardous waste disposal unit, the matching score is ; When the industry code segment and the category code segment of the hazardous waste to be treated are consistent with the corresponding code segments of the waste code of the hazardous waste disposal unit, the matching score is ; When the to-be-processed hazardous waste only the category code section is consistent with the corresponding code section of the waste code of the hazardous waste disposal unit, the matching score is ; When the category code section of the hazardous waste to be treated does not match the corresponding code section of the waste code of the hazardous waste disposal unit, the matching score is ; wherein .

5. The hazardous waste treatment facility match and warning system of claim 1 wherein: The load status factor The expression of the load status factor is as follows: ; ; wherein, is the load factor, is is the sensitivity parameter of the expression to deviation from the ideal load factor, is the vertex position determining parameter, is the maximum at the vertex parameter, is the cumulative amount of hazardous waste disposed, is the annual approved disposal amount of the hazardous waste disposal facility.

6. The hazardous waste treatment facility match and early warning system of claim 5, wherein: When the load rate is less than 50%, the early warning result is idle early warning. When the load rate is less than 0.8, the early warning result is a running normally prompt; When the load rate is greater than 1, the early warning result is saturation early warning; When the load rate is greater than 1, the early warning result is overload early warning; wherein .

7. The hazardous waste treatment facility match and warning system of claim 1 wherein: The data sources of the hazardous waste disposal facility related data include government open data, Internet of Things data and manually entered data, and the data acquisition module comprises: a government data acquisition unit for automatically capturing government open data related to hazardous waste through network crawler technology; an Internet of Things data access unit for connecting to a provincial solid waste environmental supervision information platform to obtain Internet of Things data of hazardous waste generation, storage, transfer and disposal; a manual input interface unit for receiving manually input data.

8. The hazardous waste treatment facility match and warning system of claim 1 wherein: Further comprising a data update management module for controlling the data acquisition frequency of the data acquisition module and checking the data collected by the data acquisition module.

9. The hazardous waste treatment facility match and alert system of claim 1, wherein: Further comprising a data visualization module for providing the matching result for waste-producing enterprises and the warning result for regulatory departments.

10. A hazardous waste disposal facility matching and early warning method based on multi-source data fusion, characterized in that, The method is based on the hazardous waste disposal facility matching and warning system according to any one of claims 1-9, comprising the following steps: acquiring multi-source heterogeneous data, wherein the multi-source heterogeneous data is hazardous waste disposal facility related data of multiple hazardous waste disposal units; analyzing, cleaning and standardizing the multi-source heterogeneous data to obtain standardized data; analyzing the standardized data to obtain a matching result or a warning result; in the process of obtaining the matching result, recommending a corresponding disposal unit to a waste-producing enterprise based on a multi-factor weighted scoring algorithm to generate the matching result; in the process of obtaining the warning result, generating the warning result according to a load rate; the comprehensive score S of the multi-factor weighted scoring algorithm is determined by the following factors: a distance factor determined based on the geographical distance between the waste-producing enterprise and the disposal unit; an ability matching factor determined based on the matching degree of the waste code to be disposed and the approved operating code of the disposal unit; a load state factor determined based on the current load state of the disposal unit; and a credit factor determined based on the historical operation data of the disposal unit.

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